1
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Kashafutdinova IM, Poyezzhayeva A, Gimadiev T, Madzhidov T. Active learning approaches in molecule pKi prediction. Mol Inform 2024:e202400154. [PMID: 39105614 DOI: 10.1002/minf.202400154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/21/2024] [Accepted: 06/23/2024] [Indexed: 08/07/2024]
Abstract
During the early stages of drug design, identifying compounds with suitable bioactivities is crucial. Given the vast array of potential drug databases, it's feasible to assay only a limited subset of candidates. The optimal method for selecting the candidates, aiming to minimize the overall number of assays, involves an active learning (AL) approach. In this work, we benchmarked a range of AL strategies with two main objectives: (1) to identify a strategy that ensures high model performance and (2) to select molecules with desired properties using minimal assays. To evaluate the different AL strategies, we employed the simulated AL workflow based on "virtual" experiments. These experiments leveraged ChEMBL datasets, which come with known biological activity values for the molecules. Furthermore, for classification tasks, we proposed the hybrid selection strategy that unified both exploration and exploitation AL strategies into a single acquisition function, defined by parameters n and c. We have also shown that popular minimal margin and maximal variance selection approaches for exploration selection correspond to minimization of the hybrid acquisition function with n=1 and 2 respectively. The balance between the exploration and exploitation strategies can be adjusted using a coefficient (c), making the optimal strategy selection straightforward. The primary strength of the hybrid selection method lies in its adaptability; it offers the flexibility to adjust the criteria for molecule selection based on the specific task by modifying the value of the contribution coefficient. Our analysis revealed that, in regression tasks, AL strategies didn't succeed at ensuring high model performance, however, they were successful in selecting molecules with desired properties using minimal number of tests. In analogous experiments in classification tasks, exploration strategy and the hybrid selection function with a constant c<1 (for n=1) and c≤0.2 (for n=2) were effective in achieving the goal of constructing a high-performance predictive model using minimal data. When searching for molecules with desired properties, exploitation, and the hybrid function with c≥1 (n=1) and c≥0.7 (n=2) demonstrated efficiency identifying molecules in fewer iterations compared to random selection method. Notably, when the hybrid function was set to an intermediate coefficient value (c=0.7), it successfully addressed both tasks simultaneously.
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Affiliation(s)
- I M Kashafutdinova
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, 420008, Russia
| | - A Poyezzhayeva
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, 420008, Russia
| | - T Gimadiev
- A.M. Butlerov Institute of Chemistry, Kazan Federal University, Kazan, 420008, Russia
| | - T Madzhidov
- Chemistry Solutions, Elsevier, London, EC2Y 5AS, UK
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2
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Zhang R, Nolte D, Sanchez-Villalobos C, Ghosh S, Pal R. Topological regression as an interpretable and efficient tool for quantitative structure-activity relationship modeling. Nat Commun 2024; 15:5072. [PMID: 38871711 DOI: 10.1038/s41467-024-49372-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2023] [Accepted: 06/04/2024] [Indexed: 06/15/2024] Open
Abstract
Quantitative structure-activity relationship (QSAR) modeling is a powerful tool for drug discovery, yet the lack of interpretability of commonly used QSAR models hinders their application in molecular design. We propose a similarity-based regression framework, topological regression (TR), that offers a statistically grounded, computationally fast, and interpretable technique to predict drug responses. We compare the predictive performance of TR on 530 ChEMBL human target activity datasets against the predictive performance of deep-learning-based QSAR models. Our results suggest that our sparse TR model can achieve equal, if not better, performance than the deep learning-based QSAR models and provide better intuitive interpretation by extracting an approximate isometry between the chemical space of the drugs and their activity space.
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Affiliation(s)
- Ruibo Zhang
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Daniel Nolte
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Cesar Sanchez-Villalobos
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA
| | - Souparno Ghosh
- Department of Statistics, University of Nebraska - Lincoln, Lincoln, NB, 68588, USA.
| | - Ranadip Pal
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, 79409, USA.
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3
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Salvatti BA, Chagas MA, Fernandes PO, Ladeira YFX, Bozzi AS, Valadares VS, Valente AP, de Miranda AS, Rocha WR, Maltarollo VG, Moraes AH. Understanding the Enzyme ( S)-Norcoclaurine Synthase Promiscuity to Aldehydes and Ketones. J Chem Inf Model 2024; 64:4462-4474. [PMID: 38776464 DOI: 10.1021/acs.jcim.3c01773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/25/2024]
Abstract
The (S)-norcoclaurine synthase from Thalictrum flavum (TfNCS) stereoselectively catalyzes the Pictet-Spengler reaction between dopamine and 4-hydroxyphenylacetaldehyde to give (S)-norcoclaurine. TfNCS can catalyze the Pictet-Spengler reaction with various aldehydes and ketones, leading to diverse tetrahydroisoquinolines. This substrate promiscuity positions TfNCS as a highly promising enzyme for synthesizing fine chemicals. Understanding carbonyl-containing substrates' structural and electronic signatures that influence TfNCS activity can help expand its applications in the synthesis of different compounds and aid in protein optimization strategies. In this study, we investigated the influence of the molecular properties of aldehydes and ketones on their reactivity in the TfNCS-catalyzed Pictet-Spengler reaction. Initially, we compiled a library of reactive and unreactive compounds from previous publications. We also performed enzymatic assays using nuclear magnetic resonance to identify some reactive and unreactive carbonyl compounds, which were then included in the library. Subsequently, we employed QSAR and DFT calculations to establish correlations between substrate-candidate structures and reactivity. Our findings highlight correlations of structural and stereoelectronic features, including the electrophilicity of the carbonyl group, to the reactivity of aldehydes and ketones toward the TfNCS-catalyzed Pictet-Spengler reaction. Interestingly, experimental data of seven compounds out of fifty-three did not correlate with the electrophilicity of the carbonyl group. For these seven compounds, we identified unfavorable interactions between them and the TfNCS. Our results demonstrate the applications of in silico techniques in understanding enzyme promiscuity and specificity, with a particular emphasis on machine learning methodologies, DFT electronic structure calculations, and molecular dynamic (MD) simulations.
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Affiliation(s)
- Brunno A Salvatti
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Marcelo A Chagas
- Departamento de Ciências Exatas, Universidade do Estado de Minas Gerais, João Monlevade, Minas Gerais 35930-314, Brazil
| | - Phillipe O Fernandes
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Yan F X Ladeira
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Aline S Bozzi
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Veronica S Valadares
- Departamento de Bioquímica e Imunologia, Instituto de Ciências Biológicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Ana Paula Valente
- Centro Nacional de Ressonância Magnética Nuclear, Instituto de Bioquímica Médica Leopoldo de Meis, Centro de Ciências da Saúde, Universidade Federal do Rio de Janeiro, Rio de Janeiro 21.941-902, Brazil
| | - Amanda S de Miranda
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Willian R Rocha
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Vinicius G Maltarollo
- Departamento de Produtos Farmacêuticos, Faculdade de Farmácia, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
| | - Adolfo H Moraes
- Departamento de Química, Instituto de Ciências Exatas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-901, Brazil
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4
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Holtz M, Acevedo-Rocha CG, Jensen MK. Combining enzyme and metabolic engineering for microbial supply of therapeutic phytochemicals. Curr Opin Biotechnol 2024; 87:103110. [PMID: 38503222 DOI: 10.1016/j.copbio.2024.103110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/27/2024] [Accepted: 02/29/2024] [Indexed: 03/21/2024]
Abstract
The history of pharmacology is deeply intertwined with plant-derived compounds, which continue to be crucial in drug development. However, their complex structures and limited availability in plants challenge drug discovery, optimization, development, and industrial production via chemical synthesis or natural extraction. This review delves into the integration of metabolic and enzyme engineering to leverage micro-organisms as platforms for the sustainable and reliable production of therapeutic phytochemicals. We argue that engineered microbes can serve a triple role in this paradigm: facilitating pathway discovery, acting as cell factories for scalable manufacturing, and functioning as platforms for chemical derivatization. Analyzing recent progress and outlining future directions, the review highlights microbial biotechnology's transformative potential in expanding plant-derived human therapeutics' discovery and supply chains.
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Affiliation(s)
- Maxence Holtz
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Carlos G Acevedo-Rocha
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Michael K Jensen
- Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Kgs. Lyngby, Denmark.
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5
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Karasev DA, Sobolev BN, Filimonov DA, Lagunin A. Prediction of viral protease inhibitors using proteochemometrics approach. Comput Biol Chem 2024; 110:108061. [PMID: 38574417 DOI: 10.1016/j.compbiolchem.2024.108061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 03/21/2024] [Accepted: 03/23/2024] [Indexed: 04/06/2024]
Abstract
Being widely accepted tools in computational drug search, the (Q)SAR methods have limitations related to data incompleteness. The proteochemometrics (PCM) approach expands the applicability area by using description for both protein and ligand structures. The PCM algorithms are urgently required for the development of new antiviral agents. We suggest the PCM method using the TLMNA descriptors, combining the MNA descriptors of ligands and protein sequence N-grams. Our method was validated on the viral chymotrypsin-like proteases and their ligands. We have developed an original protocol allowing us to collect a comprehensive set of 15 protein sequences and more than 9000 ligands from the ChEMBL database. The N-grams were derived from the 3D-based alignment, accurately superposing ligand-binding regions. In testing the ligand set in SAR mode with MNA descriptors, an accuracy above 0.95 was determined that shows the perspective of the antiviral drug search in virtual chemical libraries. The effective PCM models were built with the TLMNA descriptor. The strong validation procedure with pair exclusion simulated the prediction of interactions between the new ligands and new targets, resulting in accuracy estimation up to 0.89. The PCM approach shows slightly lower accuracy caused by more uncertainty compared with SAR, but it overcomes the problem of data incompleteness.
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Affiliation(s)
- Dmitry A Karasev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia.
| | - Boris N Sobolev
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Dmitry A Filimonov
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia
| | - Alexey Lagunin
- Department of Bioinformatics, Institute of Biomedical Chemistry, Moscow 119121, Russia; Department of Bioinformatics, Pirogov Russian National Research Medical University, Moscow 117997, Russia
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6
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Revanasiddappa PD, H. G. G, K. P. C, Natarajamurthy S, K. N, Pradeep S, Shivamallu C, Elossaily GM, Achar RR, Silina E, Stupin V, Manturova N, A. Shati A, Y. Alfaifi M, I. Elbehairi SE, Kestur Nagaraj A, Mahadevamurthy M, Kollur SP. Computational exploration of Picrasma quassioides compounds as CviR-mediated quorum sensing inhibitors against Chromobacterium violaceum. Front Chem 2024; 12:1286675. [PMID: 38867763 PMCID: PMC11167448 DOI: 10.3389/fchem.2024.1286675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Accepted: 04/17/2024] [Indexed: 06/14/2024] Open
Abstract
Chromobacterium violaceum an opportunistic human pathogenic bacterium, exhibits resistance to conventional antibiotics by exploiting its quorum sensing mechanism to regulate virulence factor expression. In light of this, disrupting the quorum sensing mechanism presents a promising avenue for treating infections caused by this pathogen. The study focused on using the cytoplasmic quorum sensing receptor CviR from C. violaceum as a model target to identify novel quorum sensing inhibitors from P. quassioides through in silico computational approaches. Molecular docking analyses unveiled that several phytochemicals derived from Picrasma quassioides exhibit the potential to inhibit quorum sensing by binding to CviR protein. Notably, the compounds such as Quassidine I (- 8.8 kcal/mol), Quassidine J (- 8.8 kcal/mol), Kumudine B (- 9.1 kcal/mol) and Picrasamide A (- 8.9 kcal/mol) exhibited high docking scores, indicating strong binding affinity to the CviR protein. The native ligand C6-HSL (N-hexanoyl-L-homoserine lactone) as a positive control/co-crystal inhibitor also demonstrated a significant binding energy of-7.7 kcal/mol. The molecular dynamics simulation for 200 ns showed the thermodynamic stability and binding affinity refinement of the top-ranked CviR inhibitor (Kumudine B) with its stable binding and minor fluctuations compared to positive control (C6-HSL). Pharmacokinetic predictions indicated that Kumudine B possesses favourable drug-like properties, which suggest its potential as a drug candidate. The study highlight Kumudine B as a potential agent for inhibiting the CviR protein in C. violaceum. The comprehensive evaluation of Kumudine B provides valuable insights into its pharmacological profiles, facilitating its assessment for diverse therapeutic applications and guiding future research activities, particularly as antibacterial agents for clinical drug development.
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Affiliation(s)
| | - Gowtham H. G.
- Department of Studies and Research in Food Science and Nutrition, Karnataka State Open University, Mysuru, India
| | - Chandana K. P.
- Department of Biotechnology, Siddaganga Institute of Technology, Tumkur, India
| | | | - Nataraj K.
- Department of Studies in Botany, University of Mysore, Mysore, India
| | - Sushma Pradeep
- Department of Biotechnology and Bioinformatics, School of Life Sciences, JSS Academy of Higher Education and Research, Mysuru, India
| | - Chandan Shivamallu
- Department of Biotechnology and Bioinformatics, School of Life Sciences, JSS Academy of Higher Education and Research, Mysuru, India
| | - Gehan M. Elossaily
- Department of Basic Medical Sciences, College of Medicine, AlMaarefa University, Riyadh, Saudi Arabia
| | - Raghu Ram Achar
- Division of Biochemistry, School of Life Sciences, JSS Academy of Higher Education and Research, Mysuru, India
| | - Ekaterina Silina
- Department of Pathophysiology, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia
| | - Victor Stupin
- Department of Hospital Surgery, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Natalia Manturova
- Department of Hospital Surgery, Pirogov Russian National Research Medical University, Moscow, Russia
| | - Ali A. Shati
- Biology Department, Faculty of Science, King Khalid University, Abha, Saudi Arabia
| | - Mohammad Y. Alfaifi
- Biology Department, Faculty of Science, King Khalid University, Abha, Saudi Arabia
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7
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Cao W, Wu N, Zhang S, Qi Y, Guo R, Wang Z, Qu R. Photodegradation of polychlorinated biphenyls in water/nitrogen-doped silica and air/nitrogen-doped silica systems: Kinetics, mechanism and quantitative structure activity relationship (QSAR) analysis. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 924:171586. [PMID: 38461975 DOI: 10.1016/j.scitotenv.2024.171586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 03/05/2024] [Accepted: 03/07/2024] [Indexed: 03/12/2024]
Abstract
Developing efficient and low-cost photocatalytic materials is essential for removing polychlorinated biphenyls (PCBs). In this work, the photodegradation process of fourteen representative polychlorinated biphenyls (PCBs) in both water/nitrogen-doped SiO2 (N-SiO2) and air/N-SiO2 systems was studied. The photodegradation kinetics of PCBs is consistent with the pseudo-first-order kinetic equation. The variation in the degradation effects of different PCBs in the two systems is primarily related to the position of the Cl substituent and the effective absorption wavelength range of PCBs. A total of fourteen intermediates for 4'-Dichlorobiphenyl (PCB-15), 2,2',4,4',6,6'-Hexachlorobiphenyl (PCB-155), and 2,2',3,3',4,4',5,5',6,6'-Decachlorobiphenyl (PCB-209) generated from four reaction pathways were identified based on both mass spectrometry analysis and theoretical calculations. Using the values of lnk (k denotes pseudo-first-order kinetic constants) for the 11 PCBs in the training set and the calculated molecular and structural parameters, quantitative structure-activity relationship (QSAR) models for the two systems were constructed by using multiple linear regression (MLR) method to better understand the factors affecting the photodegradation rate of PCBs. The QSAR equations were obtained with Cl atom substitution at position 3 (N3) as the main parameter, which were lnk = -1.98 - 0.19 N3 for the water/N-SiO2 system and lnk = -1.56 - 0.34 N3 for the air/N-SiO2 system, with the correlation coefficient (R2) of 0.66 and 0.73, leave-one-out cross-validation (Q2LOO) of 0.51 and 0.59, respectively, and bootstrapping validation coefficients (Q2BOOT) values of both 0.74, confirming that the models were well fitted and showed high robustness and prediction ability. This study provides valuable insights into photocatalytic degradation studies of PCBs.
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Affiliation(s)
- Wenqian Cao
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Jiangsu, Nanjing 210023, PR China
| | - Nannan Wu
- State Key Laboratory for Managing Biotic and Chemical Threats to the Quality and Safety of Agro-products, Institute of Agro-product Safety and Nutrition, Zhejiang Academy of Agricultural Sciences, Hangzhou 310021, PR China
| | - Shengnan Zhang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Jiangsu, Nanjing 210023, PR China
| | - Yumeng Qi
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Jiangsu, Nanjing 210023, PR China
| | - Ruixue Guo
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Jiangsu, Nanjing 210023, PR China
| | - Zunyao Wang
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Jiangsu, Nanjing 210023, PR China
| | - Ruijuan Qu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Jiangsu, Nanjing 210023, PR China.
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8
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Sutthibutpong T, Posansee K, Liangruksa M, Termsaithong T, Piyayotai S, Phitsuwan P, Saparpakorn P, Hannongbua S, Laomettachit T. Combining Deep Learning and Structural Modeling to Identify Potential Acetylcholinesterase Inhibitors from Hericium erinaceus. ACS OMEGA 2024; 9:16311-16321. [PMID: 38617639 PMCID: PMC11007777 DOI: 10.1021/acsomega.3c10459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/16/2024] [Accepted: 03/13/2024] [Indexed: 04/16/2024]
Abstract
Alzheimer's disease (AD) is the most common type of dementia, affecting over 50 million people worldwide. Currently, most approved medications for AD inhibit the activity of acetylcholinesterase (AChE), but these treatments often come with harmful side effects. There is growing interest in the use of natural compounds for disease prevention, alleviation, and treatment. This trend is driven by the anticipation that these substances may incur fewer side effects than existing medications. This research presents a computational approach combining machine learning with structural modeling to discover compounds from medicinal mushrooms with a high potential to inhibit the activity of AChE. First, we developed a deep neural network capable of rapidly screening a vast number of compounds to indicate their potential to inhibit AChE activity. Subsequently, we applied deep learning models to screen the compounds in the BACMUSHBASE database, which catalogs the bioactive compounds from cultivated and wild mushroom varieties local to Thailand, resulting in the identification of five promising compounds. Next, the five identified compounds underwent molecular docking techniques to calculate the binding energy between the compounds and AChE. This allowed us to refine the selection to two compounds, erinacerin A and hericenone B. Further analysis of the binding energy patterns between these compounds and the target protein revealed that both compounds displayed binding energy profiles similar to the combined characteristics of donepezil and galanthamine, the prescription drugs for AD. We propose that these two compounds, derived from Hericium erinaceus (also known as lion's mane mushroom), are suitable candidates for further research and development into symptom-alleviating AD medications.
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Affiliation(s)
- Thana Sutthibutpong
- Center
of Excellence in Theoretical and Computational Science (TaCS-CoE),
Faculty of Science, King Mongkut’s
University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand
- Theoretical
and Computational Physics Group, Department of Physics, King Mongkut’s University of Technology Thonburi
(KMUTT), Bangkok 10140, Thailand
| | - Kewalin Posansee
- Theoretical
and Computational Physics Group, Department of Physics, King Mongkut’s University of Technology Thonburi
(KMUTT), Bangkok 10140, Thailand
| | - Monrudee Liangruksa
- National
Nanotechnology Center (NANOTEC), National
Science and Technology Development Agency (NSTDA), Pathum Thani 12120, Thailand
| | - Teerasit Termsaithong
- Center
of Excellence in Theoretical and Computational Science (TaCS-CoE),
Faculty of Science, King Mongkut’s
University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand
- Theoretical
and Computational Physics Group, Department of Physics, King Mongkut’s University of Technology Thonburi
(KMUTT), Bangkok 10140, Thailand
- Learning
Institute, King Mongkut’s University
of Technology Thonburi (KMUTT), Bangkok 10140, Thailand
| | - Supanida Piyayotai
- Learning
Institute, King Mongkut’s University
of Technology Thonburi (KMUTT), Bangkok 10140, Thailand
| | - Paripok Phitsuwan
- Division
of Biochemical Technology, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok 10150, Thailand
| | | | - Supa Hannongbua
- Department
of Chemistry, Faculty of Science, Kasetsart
University, Bangkok 10900, Thailand
| | - Teeraphan Laomettachit
- Center
of Excellence in Theoretical and Computational Science (TaCS-CoE),
Faculty of Science, King Mongkut’s
University of Technology Thonburi (KMUTT), Bangkok 10140, Thailand
- Theoretical
and Computational Physics Group, Department of Physics, King Mongkut’s University of Technology Thonburi
(KMUTT), Bangkok 10140, Thailand
- Bioinformatics
and Systems Biology Program, School of Bioresources and Technology, King Mongkut’s University of Technology Thonburi, Bangkok 10150, Thailand
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9
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Łapińska N, Pacławski A, Szlęk J, Mendyk A. SerotoninAI: Serotonergic System Focused, Artificial Intelligence-Based Application for Drug Discovery. J Chem Inf Model 2024; 64:2150-2157. [PMID: 38289046 PMCID: PMC11005036 DOI: 10.1021/acs.jcim.3c01517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Revised: 01/02/2024] [Accepted: 01/04/2024] [Indexed: 04/09/2024]
Abstract
SerotoninAI is an innovative web application for scientific purposes focused on the serotonergic system. By leveraging SerotoninAI, researchers can assess the affinity (pKi value) of a molecule to all main serotonin receptors and serotonin transporters based on molecule structure introduced as SMILES. Additionally, the application provides essential insights into critical attributes of potential drugs such as blood-brain barrier penetration and human intestinal absorption. The complexity of the serotonergic system demands advanced tools for accurate predictions, which is a fundamental requirement in drug development. SerotoninAI addresses this need by providing an intuitive user interface that generates predictions of pKi values for the main serotonergic targets. The application is freely available on the Internet at https://serotoninai.streamlit.app/, implemented in Streamlit with all major web browsers supported. Currently, to the best of our knowledge, there is no tool that allows users to access affinity predictions for serotonergic targets without registration or financial obligations. SerotoninAI significantly increases the scope of drug development activities worldwide. The source code of the application is available at https://github.com/nczub/SerotoninAI_streamlit.
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Affiliation(s)
- Natalia Łapińska
- Department
of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, 30-688 Kraków, Poland
- Doctoral
School of Medicinal and Health Sciences, Jagiellonian University Medical College, 30-688 Kraków, Poland
| | - Adam Pacławski
- Department
of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, 30-688 Kraków, Poland
| | - Jakub Szlęk
- Department
of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, 30-688 Kraków, Poland
| | - Aleksander Mendyk
- Department
of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University Medical College, 30-688 Kraków, Poland
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10
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Oliveira PF, Guedes RC, Falcao AO. Inferring molecular inhibition potency with AlphaFold predicted structures. Sci Rep 2024; 14:8252. [PMID: 38589418 PMCID: PMC11001998 DOI: 10.1038/s41598-024-58394-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/28/2024] [Indexed: 04/10/2024] Open
Abstract
Even though in silico drug ligand-based methods have been successful in predicting interactions with known target proteins, they struggle with new, unassessed targets. To address this challenge, we propose an approach that integrates structural data from AlphaFold 2 predicted protein structures into machine learning models. Our method extracts 3D structural protein fingerprints and combines them with ligand structural data to train a single machine learning model. This model captures the relationship between ligand properties and the unique structural features of various target proteins, enabling predictions for never before tested molecules and protein targets. To assess our model, we used a dataset of 144 Human G-protein Coupled Receptors (GPCRs) with over 140,000 measured inhibition constants (Ki) values. Results strongly suggest that our approach performs as well as state-of-the-art ligand-based methods. In a second modeling approach that used 129 targets for training and a separate test set of 15 different protein targets, our model correctly predicted interactions for 73% of targets, with explained variances exceeding 0.50 in 22% of cases. Our findings further verified that the usage of experimentally determined protein structures produced models that were statistically indistinct from the Alphafold synthetic structures. This study presents a proteo-chemometric drug screening approach that uses a simple and scalable method for extracting protein structural information for usage in machine learning models capable of predicting protein-molecule interactions even for orphan targets.
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Affiliation(s)
- Pedro F Oliveira
- Lasige, Faculdade de Ciências, Universidade de Lisboa, 1749-016 Lisboa, Portugal
| | - Rita C Guedes
- Research Institute for Medicines (iMed.ULisboa), Faculdade de Farmácia, Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-003 Lisboa, Portugal
| | - Andre O Falcao
- Departamento de Informática, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal.
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11
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Wang K, Kim N, Bagherian M, Li K, Chou E, Colacino JA, Dolinoy DC, Sartor MA. Gene Target Prediction of Environmental Chemicals Using Coupled Matrix-Matrix Completion. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:5889-5898. [PMID: 38501580 PMCID: PMC11131040 DOI: 10.1021/acs.est.4c00458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
Human exposure to toxic chemicals presents a huge health burden. Key to understanding chemical toxicity is knowledge of the molecular target(s) of the chemicals. Because a comprehensive safety assessment for all chemicals is infeasible due to limited resources, a robust computational method for discovering targets of environmental exposures is a promising direction for public health research. In this study, we implemented a novel matrix completion algorithm named coupled matrix-matrix completion (CMMC) for predicting direct and indirect exposome-target interactions, which exploits the vast amount of accumulated data regarding chemical exposures and their molecular targets. Our approach achieved an AUC of 0.89 on a benchmark data set generated using data from the Comparative Toxicogenomics Database. Our case studies with bisphenol A and its analogues, PFAS, dioxins, PCBs, and VOCs show that CMMC can be used to accurately predict molecular targets of novel chemicals without any prior bioactivity knowledge. Our results demonstrate the feasibility and promise of computationally predicting environmental chemical-target interactions to efficiently prioritize chemicals in hazard identification and risk assessment.
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Affiliation(s)
- Kai Wang
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Nicole Kim
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maryam Bagherian
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI 48109, USA
| | - Kai Li
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Elysia Chou
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Justin A. Colacino
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Dana C. Dolinoy
- Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
| | - Maureen A. Sartor
- Department of Computational Medicine and Bioinformatics, School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI 48109, USA
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12
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Ding H, Xing F, Zou L, Zhao L. QSAR analysis of VEGFR-2 inhibitors based on machine learning, Topomer CoMFA and molecule docking. BMC Chem 2024; 18:59. [PMID: 38555462 PMCID: PMC10981835 DOI: 10.1186/s13065-024-01165-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 03/12/2024] [Indexed: 04/02/2024] Open
Abstract
VEGFR-2 kinase inhibitors are clinically approved drugs that can effectively target cancer angiogenesis. However, such inhibitors have adverse effects such as skin toxicity, gastrointestinal reactions and hepatic impairment. In this study, machine learning and Topomer CoMFA, which is an alignment-dependent, descriptor-based method, were employed to build structural activity relationship models of potentially new VEGFR-2 inhibitors. The prediction ac-curacy of the training and test sets of the 2D-SAR model were 82.4 and 80.1%, respectively, with KNN. Topomer CoMFA approach was then used for 3D-QSAR modeling of VEGFR-2 inhibitors. The coefficient of q2 for cross-validation of the model 1 was greater than 0.5, suggesting that a stable drug activity-prediction model was obtained. Molecular docking was further performed to simulate the interactions between the five most promising compounds and VEGFR-2 target protein and the Total Scores were all greater than 6, indicating that they had a strong hydrogen bond interactions were present. This study successfully used machine learning to obtain five potentially novel VEGFR-2 inhibitors to increase our arsenal of drugs to combat cancer.
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Affiliation(s)
- Hao Ding
- Department of Ultrasound, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China
| | - Fei Xing
- Department of Oncology, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China
| | - Lin Zou
- Medical College of Guangxi University, Nanning, 530004, Guangxi, China
| | - Liang Zhao
- Hepatobiliary and Splenic Surgery Ward, Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, 110004, Liaoning, China.
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13
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Zdybel S, Sosnowska A, Kowalska D, Sommer J, Conrady B, Mester P, Gromelski M, Puzyn T. Hybrid Machine Learning and Experimental Studies of Antiviral Potential of Ionic Liquids against P100, MS2, and Phi6. J Chem Inf Model 2024; 64:1996-2007. [PMID: 38452014 DOI: 10.1021/acs.jcim.3c02037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2024]
Abstract
Viruses are a group of widespread organisms that are often responsible for very dangerous diseases, as most of them follow a mechanism to multiply and infect their hosts as quickly as possible. Pathogen viruses also mutate regularly, with the result that measures to prevent virus transmission and recover from the disease caused are often limited. The development of new substances is very time-consuming and highly budgeted and requires the sacrifice of many living organisms. Computational chemistry methods allow faster analysis at a much lower cost and, most importantly, reduce the number of living organisms sacrificed experimentally to a minimum. Ionic liquids (ILs) are a group of chemical compounds that could potentially find a wide range of applications due to their potential virucidal activity. In our study, we conducted a complex computational analysis to predict the antiviral activity of ionic liquids against three surrogate viruses: two nonenveloped viruses, Listeria monocytogenes phage P100 and Escherichia coli phage MS2, and one enveloped virus, Pseudomonas syringae phage Phi6. Based on experimental data of toxic activity (logEC90), we assigned activity classes to 154 ILs. Prediction models were created and validated according to the Organization for Economic Co-operation and Development (OECD) recommendations using the Classification Tree method. Further, we performed an external validation of our models through virtual screening on a set of 1277 theoretically generated ionic liquids and then selected 10 active ionic liquids, which were synthesized to verify their activity against the analyzed viruses. Our study proved the effectiveness and efficiency of computational methods to predict the antiviral activity of ionic liquids. Thus, computational models are a cost-effective alternative approach compared with time-consuming experimental studies where live animals are involved.
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Affiliation(s)
- Szymon Zdybel
- QSAR Lab, ul. Trzy Lipy 3, 80-172 Gdańsk, Poland
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdańsk, Poland
| | - Anita Sosnowska
- QSAR Lab, ul. Trzy Lipy 3, 80-172 Gdańsk, Poland
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdańsk, Poland
| | | | - Julia Sommer
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 Vienna, Austria
| | - Beate Conrady
- Department of Veterinary and Animal Sciences, University of Copenhagen, Grønnegårdsvej 8, 1870 Frederiksberg Campus, Copenhagen DK-1870, Denmark
| | - Patrick Mester
- Unit of Food Microbiology, Institute of Food Safety, Food Technology and Veterinary Public Health, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine Vienna, Veterinärplatz 1, 1210 Vienna, Austria
| | | | - Tomasz Puzyn
- QSAR Lab, ul. Trzy Lipy 3, 80-172 Gdańsk, Poland
- Laboratory of Environmental Chemometrics, Faculty of Chemistry, University of Gdansk, 80-308 Gdańsk, Poland
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14
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Kumar A, Ojha PK, Roy K. First report on pesticide sub-chronic and chronic toxicities against dogs using QSAR and chemical read-across. SAR AND QSAR IN ENVIRONMENTAL RESEARCH 2024; 35:241-263. [PMID: 38390626 DOI: 10.1080/1062936x.2024.2320143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 02/12/2024] [Indexed: 02/24/2024]
Abstract
Excessive use of chemicals is the outcome of the industrialization of agricultural sectors which leads to disturbance of ecological balance. Various agrochemicals are widely used in agricultural fields, urban green areas, and to protect from various pest-associated diseases. Due to their long-term health and environmental hazards, chronic toxicity assessment is crucial. Since in vivo and in vitro toxicity assessments are costly, lengthy, and require a large number of animal experiments, in silico toxicity approaches are better alternatives to save time, cost, and animal experimentation. We have developed the first regression-based 2D-QSAR models using different sub-chronic and chronic toxicity data of pesticides against dogs employing 2D descriptors. From the statistical results (n train = 53 - 62 , r 2 = 0.614 to 0.754, Q L O O 2 = 0.501 to 0.703 and Q F 1 2 = 0.531 to 0.718, Q F 2 2 = 0.523 - 0.713 ), it was concluded that the models are robust, reliable, interpretable, and predictive. Similarity-based read-across algorithm was also used to improve the predictivity (Q F 1 2 = 0.595 - 0.813 , Q F 2 2 = 0.573 - 0.809 ) of the models. 5132 chemicals obtained from the CPDat and 1694 pesticides obtained from the PPDB database were also screened using the developed models, and their predictivity and reliability were checked. Thus, these models will be helpful for eco-toxicological data-gap filling, toxicity prediction of untested pesticides, and development of novel, safer & eco-friendly pesticides.
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Affiliation(s)
- A Kumar
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - P K Ojha
- Drug Discovery and Development Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
| | - K Roy
- Drug Theoretics and Cheminformatics (DTC) Laboratory, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, India
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15
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Mora JR, Marquez EA, Pérez-Pérez N, Contreras-Torres E, Perez-Castillo Y, Agüero-Chapin G, Martinez-Rios F, Marrero-Ponce Y, Barigye SJ. Rethinking the applicability domain analysis in QSAR models. J Comput Aided Mol Des 2024; 38:9. [PMID: 38351144 DOI: 10.1007/s10822-024-00550-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
Notwithstanding the wide adoption of the OECD principles (or best practices) for QSAR modeling, disparities between in silico predictions and experimental results are frequent, suggesting that model predictions are often too optimistic. Of these OECD principles, the applicability domain (AD) estimation has been recognized in several reports in the literature to be one of the most challenging, implying that the actual reliability measures of model predictions are often unreliable. Applying tree-based error analysis workflows on 5 QSAR models reported in the literature and available in the QsarDB repository, i.e., androgen receptor bioactivity (agonists, antagonists, and binders, respectively) and membrane permeability (highest membrane permeability and the intrinsic permeability), we demonstrate that predictions erroneously tagged as reliable (AD prediction errors) overwhelmingly correspond to instances in subspaces (cohorts) with the highest prediction error rates, highlighting the inhomogeneity of the AD space. In this sense, we call for more stringent AD analysis guidelines which require the incorporation of model error analysis schemes, to provide critical insight on the reliability of underlying AD algorithms. Additionally, any selected AD method should be rigorously validated to demonstrate its suitability for the model space over which it is applied. These steps will ultimately contribute to more accurate estimations of the reliability of model predictions. Finally, error analysis may also be useful in "rational" model refinement in that data expansion efforts and model retraining are focused on cohorts with the highest error rates.
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Affiliation(s)
- Jose R Mora
- Departamento de Ingeniería Química, Universidad San Francisco de Quito (USFQ), Instituto de Simulación Computacional (ISC- USFQ), Diego de Robles y Vía Interoceánica, Quito, 170901, Ecuador
| | - Edgar A Marquez
- Grupo de Investigaciones en Química Y Biología, Departamento de Química Y Biología, Facultad de Ciencias Básicas, Universidad del Norte, Carrera 51B, Km 5, vía Puerto Colombia, Barranquilla, 081007, Colombia
- Departamento de Ciencias de la Computación, Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE), Cátedras Conacyt, Ensenada, Baja California, México
| | - Noel Pérez-Pérez
- Colegio de Ciencias e Ingenierías "El Politécnico", Universidad San Francisco de Quito (USFQ), Quito, Ecuador
| | - Ernesto Contreras-Torres
- Grupo de Medicina Molecular y Traslacional (MeM&T), Universidad San Francisco de Quito, Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Av. Interoceánica Km 12 1/2 y Av. Florencia, 17, Quito, 1200-841, Ecuador
| | - Yunierkis Perez-Castillo
- Bio-Chemoinformatics Research Group, Escuela de Ciencias Físicas y Matemáticas, Universidad de Las Américas, Quito, 170504, Ecuador
| | - Guillermin Agüero-Chapin
- CIIMAR - Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n, Porto, 4450-208, Portugal
- Department of Biology, Faculty of Sciences, University of Porto, Rua do Campo Alegre, Porto, 4169- 007, Portugal
| | - Felix Martinez-Rios
- Facultad de Ingeniería, Universidad Panamericana, CDMX, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, Ciudad de México, 03920, México
| | - Yovani Marrero-Ponce
- Grupo de Medicina Molecular y Traslacional (MeM&T), Universidad San Francisco de Quito, Escuela de Medicina, Colegio de Ciencias de la Salud (COCSA), Av. Interoceánica Km 12 1/2 y Av. Florencia, 17, Quito, 1200-841, Ecuador
- Facultad de Ingeniería, Universidad Panamericana, CDMX, Augusto Rodin No. 498, Insurgentes Mixcoac, Benito Juárez, Ciudad de México, 03920, México
- Computer-Aided Molecular "Biosilico" Discovery and Bioinformatics Research International Network (CAMD-BIR IN), Cumbayá, Quito, Ecuador
| | - Stephen J Barigye
- Departamento de Química Física Aplicada, Facultad de Ciencias, Universidad Autónoma de Madrid (UAM), Madrid, 28049, Spain.
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16
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Zhou N, Zheng C, Tan H, Luo L. Identification of PLK1-PBD Inhibitors from the Library of Marine Natural Products: 3D QSAR Pharmacophore, ADMET, Scaffold Hopping, Molecular Docking, and Molecular Dynamics Study. Mar Drugs 2024; 22:83. [PMID: 38393054 PMCID: PMC10890274 DOI: 10.3390/md22020083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/03/2024] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
PLK1 is found to be highly expressed in various types of cancers, but the development of inhibitors for it has been slow. Most inhibitors are still in clinical stages, and many lack the necessary selectivity and anti-tumor effects. This study aimed to create new inhibitors for the PLK1-PBD by focusing on the PBD binding domain, which has the potential for greater selectivity. A 3D QSAR model was developed using a dataset of 112 compounds to evaluate 500 molecules. ADMET prediction was then used to select three molecules with strong drug-like characteristics. Scaffold hopping was employed to reconstruct 98 new compounds with improved drug-like properties and increased activity. Molecular docking was used to compare the efficient compound abbapolin, confirming the high-activity status of [(14S)-14-hydroxy-14-(pyridin-2-yl)tetradecyl]ammonium,[(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium. Molecular dynamics simulations and MMPBSA were conducted to evaluate the stability of the compounds in the presence of proteins. An in-depth analysis of [(14S)-15-(2-furyl)-14-hydroxypentadecyl]ammonium and [(14S)-14-hydroxy-14-phenyltetradecyl]ammonium identified them as potential candidates for PLK1 inhibitors.
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Affiliation(s)
- Nan Zhou
- The First Clinical College, Guangdong Medical University, Zhanjiang 524023, China; (N.Z.); (C.Z.); (H.T.)
| | - Chuangze Zheng
- The First Clinical College, Guangdong Medical University, Zhanjiang 524023, China; (N.Z.); (C.Z.); (H.T.)
| | - Huiting Tan
- The First Clinical College, Guangdong Medical University, Zhanjiang 524023, China; (N.Z.); (C.Z.); (H.T.)
| | - Lianxiang Luo
- The Marine Biomedical Research Institute, School of Ocean and Tropical Medicine, Guangdong Medical University, Zhanjiang 524023, China
- The Marine Biomedical Research Institute of Guangdong Zhanjiang, Zhanjiang 524023, China
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17
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Lei L, Zhang L, Han Z, Chen Q, Liao P, Wu D, Tai J, Xie B, Su Y. Advancing chronic toxicity risk assessment in freshwater ecology by molecular characterization-based machine learning. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 342:123093. [PMID: 38072027 DOI: 10.1016/j.envpol.2023.123093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 11/30/2023] [Accepted: 12/02/2023] [Indexed: 01/26/2024]
Abstract
The continuously increased production of various chemicals and their release into environments have raised potential negative effects on ecological health. However, traditional labor-intensive assessment methods cannot effectively and rapidly evaluate these hazards, especially for chronic risk. In this study, machine learning (ML) was employed to construct quantitative structure-activity relationship (QSAR) models, enabling the prediction of chronic toxicity to aquatic organisms by leveraging the molecular characteristics of pollutants, namely, the molecular descriptors, fingerprints, and graphs. The limited dataset size hindered the notable advantages of the graph attention network (GAT) model for the molecular graphs. Considering computational efficiency and performance (R2 = 0.78; RMSE = 0.77), XGBoost (XGB) was used for reliable QSAR-ML models predicting chronic toxicity using small- or medium-sized tabular data and the molecular descriptors. Further kernel density estimation analysis confirmed the high accuracy of the model for pollutant concentrations ranging from 10-3 to 102 mg/L, effectively aligning with most environmental scenarios. Model interpretation showed SlogP and exposure duration as the primary influential factors. SlogP, representing the distribution coefficient of a molecule between lipophilic and hydrophilic environments, had a negative effect on the toxicity outcomes. Additionally, the exposure duration played a crucial role in determining the chronic toxicity. Finally, the chronic toxicity data of bisphenol A validated the robustness and reliability of the model established in this research. Our study provided a robust and feasible methodology for chronic ecological risk evaluation of various types of pollutants and could facilitate and increase the use of ML applications in environmental fields.
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Affiliation(s)
- Lang Lei
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Liangmao Zhang
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Zhibang Han
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Qirui Chen
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Pengcheng Liao
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China
| | - Dong Wu
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China
| | - Jun Tai
- Shanghai Environmental Sanitation Engineering Design Institute Co., Ltd., Shanghai, 200232, China
| | - Bing Xie
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China
| | - Yinglong Su
- Shanghai Engineering Research Center of Biotransformation of Organic Solid Waste, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, 200241, China; Chongqing Key Laboratory of Precision Optics, Chongqing Institute of East China Normal University, Chongqing, 401120, China; Shanghai Institute of Pollution Control and Ecological Security, Shanghai, 200092, China.
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18
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Gallagher A, Kar S. Unveiling first report on in silico modeling of aquatic toxicity of organic chemicals to Labeo rohita (Rohu) employing QSAR and q-RASAR. CHEMOSPHERE 2024; 349:140810. [PMID: 38029938 DOI: 10.1016/j.chemosphere.2023.140810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 11/22/2023] [Accepted: 11/23/2023] [Indexed: 12/01/2023]
Abstract
Labeo rohita, a fish species within the Carp family, holds significant dietary and aquacultural importance in South Asian countries. However, the habitats of L. rohita often face exposure to various harmful pesticides and organic compounds originating from industrial and agricultural runoff. It is challenging to individually investigate the effects of each potentially harmful compound. In such cases, in silico techniques like Quantitative Structure-Activity Relationship (QSAR) and quantitative Read-Across Structure-Activity Relationship (q-RASAR) can be employed to construct algorithmic models capable of simultaneously assessing the toxicity of numerous compounds. We utilized the US EPA's ToxValDB database to curate data regarding acute median lethal concentration (LC50) toxicity for L. rohita. The experimental variables included study type (mortality), study duration (ranging from 0.25 h to 4 h), exposure route (static, flowthrough, and renewal), exposure method (drinking water), and types of chemicals (industrial chemicals and pharmaceuticals). Using this dataset, we developed regression-based QSAR and q-RASAR models to predict chemical toxicity to L. rohita based on chemical descriptors. The key descriptors for predicting the toxicity of L. rohita in the regression-based QSAR model include F05[S-Cl], SpMax_EA(ri), s4_relPathLength_2, and SpDiam_AEA(ed). These descriptors can be employed to estimate the toxicity of untested compounds and aid in the development of compounds with lower toxicity based on the presence or absence of these descriptors. Both the QSAR and q-RASAR models serve as valuable tools for understanding the chemicals' structural features responsible for toxicity and for filling gaps in aquatic toxicity data by predicting the toxicity of newly untested compounds in relation to L. rohita. Finally, the developed best model was employed to predict 297 external chemicals, the most toxic substances to L. rohita were identified as cyhalothrin, isobornyl thiocyanatoacetate, and paclobutrzol, while the least toxic ones included ethyl acetate, ethylthiourea, and n-butyric acid.
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Affiliation(s)
- Andrea Gallagher
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ, 07083, USA
| | - Supratik Kar
- Chemometrics and Molecular Modeling Laboratory, Department of Chemistry, Kean University, 1000 Morris Avenue, Union, NJ, 07083, USA.
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19
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Chunarkar-Patil P, Kaleem M, Mishra R, Ray S, Ahmad A, Verma D, Bhayye S, Dubey R, Singh HN, Kumar S. Anticancer Drug Discovery Based on Natural Products: From Computational Approaches to Clinical Studies. Biomedicines 2024; 12:201. [PMID: 38255306 PMCID: PMC10813144 DOI: 10.3390/biomedicines12010201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Revised: 01/01/2024] [Accepted: 01/10/2024] [Indexed: 01/24/2024] Open
Abstract
Globally, malignancies cause one out of six mortalities, which is a serious health problem. Cancer therapy has always been challenging, apart from major advances in immunotherapies, stem cell transplantation, targeted therapies, hormonal therapies, precision medicine, and palliative care, and traditional therapies such as surgery, radiation therapy, and chemotherapy. Natural products are integral to the development of innovative anticancer drugs in cancer research, offering the scientific community the possibility of exploring novel natural compounds against cancers. The role of natural products like Vincristine and Vinblastine has been thoroughly implicated in the management of leukemia and Hodgkin's disease. The computational method is the initial key approach in drug discovery, among various approaches. This review investigates the synergy between natural products and computational techniques, and highlights their significance in the drug discovery process. The transition from computational to experimental validation has been highlighted through in vitro and in vivo studies, with examples such as betulinic acid and withaferin A. The path toward therapeutic applications have been demonstrated through clinical studies of compounds such as silvestrol and artemisinin, from preclinical investigations to clinical trials. This article also addresses the challenges and limitations in the development of natural products as potential anti-cancer drugs. Moreover, the integration of deep learning and artificial intelligence with traditional computational drug discovery methods may be useful for enhancing the anticancer potential of natural products.
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Affiliation(s)
- Pritee Chunarkar-Patil
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (Deemed to be University), Pune 411046, Maharashtra, India
| | - Mohammed Kaleem
- Department of Pharmacology, Dadasaheb Balpande, College of Pharmacy, Nagpur 440037, Maharashtra, India;
| | - Richa Mishra
- Department of Computer Engineering, Parul University, Ta. Waghodia, Vadodara 391760, Gujarat, India;
| | - Subhasree Ray
- Department of Life Science, Sharda School of Basic Sciences and Research, Greater Noida 201310, Uttar Pradesh, India
| | - Aftab Ahmad
- Health Information Technology Department, The Applied College, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Pharmacovigilance and Medication Safety Unit, Center of Research Excellence for Drug Research and Pharmaceutical Industries, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Devvret Verma
- Department of Biotechnology, Graphic Era (Deemed to be University), Dehradun 248002, Uttarkhand, India;
| | - Sagar Bhayye
- Department of Bioinformatics, Rajiv Gandhi Institute of IT and Biotechnology, Bharati Vidyapeeth (Deemed to be University), Pune 411046, Maharashtra, India
| | - Rajni Dubey
- Division of Cardiology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei 11031, Taiwan
| | - Himanshu Narayan Singh
- Department of Systems Biology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Sanjay Kumar
- Biological and Bio-Computational Lab, Department of Life Science, Sharda School of Basic Sciences and Research, Sharda University, Greater Noida 201310, Uttar Pradesh, India
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20
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Melancon K, Pliushcheuskaya P, Meiler J, Künze G. Targeting ion channels with ultra-large library screening for hit discovery. Front Mol Neurosci 2024; 16:1336004. [PMID: 38249296 PMCID: PMC10796734 DOI: 10.3389/fnmol.2023.1336004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 12/05/2023] [Indexed: 01/23/2024] Open
Abstract
Ion channels play a crucial role in a variety of physiological and pathological processes, making them attractive targets for drug development in diseases such as diabetes, epilepsy, hypertension, cancer, and chronic pain. Despite the importance of ion channels in drug discovery, the vastness of chemical space and the complexity of ion channels pose significant challenges for identifying drug candidates. The use of in silico methods in drug discovery has dramatically reduced the time and cost of drug development and has the potential to revolutionize the field of medicine. Recent advances in computer hardware and software have enabled the screening of ultra-large compound libraries. Integration of different methods at various scales and dimensions is becoming an inevitable trend in drug development. In this review, we provide an overview of current state-of-the-art computational chemistry methodologies for ultra-large compound library screening and their application to ion channel drug discovery research. We discuss the advantages and limitations of various in silico techniques, including virtual screening, molecular mechanics/dynamics simulations, and machine learning-based approaches. We also highlight several successful applications of computational chemistry methodologies in ion channel drug discovery and provide insights into future directions and challenges in this field.
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Affiliation(s)
- Kortney Melancon
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
| | | | - Jens Meiler
- Department of Chemistry, Vanderbilt University, Nashville, TN, United States
- Center for Structural Biology, Vanderbilt University, Nashville, TN, United States
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
| | - Georg Künze
- Medical Faculty, Institute for Drug Discovery, Leipzig University, Leipzig, Germany
- Center for Scalable Data Analytics and Artificial Intelligence, Leipzig University, Leipzig, Germany
- Interdisciplinary Center for Bioinformatics, Leipzig University, Leipzig, Germany
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21
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Kamal S, Shahzad A, Rehman K, Tariq K, Akash MSH, Imran M, Assiri MA. Therapeutic Intervention of Serine Protease Inhibitors against Hepatitis C Virus. Curr Med Chem 2024; 31:2052-2072. [PMID: 37855348 DOI: 10.2174/0109298673234823230921090431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 05/12/2023] [Accepted: 05/23/2023] [Indexed: 10/20/2023]
Abstract
Hepatitis C virus (HCV) is a globally prevalent and hazardous disorder that is responsible for inducing several persistent and potentially fatal liver diseases. Current treatment strategies offer limited efficacy, often accompanied by severe and debilitating adverse effects. Consequently, there is an urgent and compelling need to develop novel therapeutic interventions that can provide maximum efficacy in combating HCV while minimizing the burden of adverse effects on patients. One promising target against HCV is the NS3-4A serine protease, a complex composed of two HCV-encoded proteins. This non-covalent heterodimer is crucial in the viral life cycle and has become a primary focus for therapeutic interventions. Although peginterferon, combined with ribavirin, is commonly employed for HCV treatment, its efficacy is hampered by significant adverse effects that can profoundly impact patients' quality of life. In recent years, the development of direct-acting antiviral agents (DAAs) has emerged as a breakthrough in HCV therapy. These agents exhibit remarkable potency against the virus and have demonstrated fewer adverse effects when combined with other DAAs. However, it is important to note that there is a potential for developing resistance to DAAs due to alterations in the amino acid position of the NS3-4A protease. This emphasizes the need for ongoing research to identify strategies that can minimize the emergence of resistance and ensure long-term effectiveness. While the combination of DAAs holds promise for HCV treatment, it is crucial to consider the possibility of drug-drug interactions. These interactions may occur when different DAAs are used concurrently, potentially compromising their therapeutic efficacy. Therefore, carefully evaluating and monitoring potential drug interactions are vital to optimize treatment outcomes. In the pursuit of novel therapeutic interventions for HCV, the field of computational biology and bioinformatics has emerged as a valuable tool. These advanced technologies and methodologies enable the development and design of new drugs and therapeutic agents that exhibit maximum efficacy, reduced risk of resistance, and minimal adverse effects. By leveraging computational approaches, researchers can efficiently screen and optimize potential candidates, accelerating the discovery and development of highly effective treatments for HCV, treatments.
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Affiliation(s)
- Shagufta Kamal
- Department of Biochemistry, Government College University, Faisalabad, Pakistan
| | - Asif Shahzad
- Department of Biochemistry, Government College University, Faisalabad, Pakistan
| | - Kanwal Rehman
- Department of Pharmacy, The Women University, Multan, Pakistan
| | - Komal Tariq
- Department of Biochemistry, Government College University, Faisalabad, Pakistan
| | | | - Muhammad Imran
- Research center for Advanced Materials Science (RCAMS), King Khalid University, Abha, Saudi Arabia
- Department of Chemistry, Faculty of Science, King Khalid University, Abha, Saudi Arabia
| | - Mohammed Ali Assiri
- Research center for Advanced Materials Science (RCAMS), King Khalid University, Abha, Saudi Arabia
- Department of Chemistry, Faculty of Science, King Khalid University, Abha, Saudi Arabia
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22
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Del Hoyo D, Salinas M, Lomas A, Ulzurrun E, Campillo NE, Sorzano CO. Scipion-Chem: An Open Platform for Virtual Drug Screening. J Chem Inf Model 2023; 63:7873-7885. [PMID: 38052452 PMCID: PMC10751785 DOI: 10.1021/acs.jcim.3c01085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 11/07/2023] [Accepted: 11/10/2023] [Indexed: 12/07/2023]
Abstract
Virtual drug screening (VDS) tackles the problem of drug discovery by computationally reducing the number of potential pharmacological molecules that need to be tested experimentally to find a new drug. To do so, several approaches have been developed through the years, typically focusing on either the physicochemical characteristics of the receptor structure (structure-based virtual screening) or those of the potential ligands (ligand-based virtual screening). Scipion is a workflow engine well suited for structural studies of biological macromolecules. Here, we present Scipion-chem, a new branch oriented to VDS. A total of 11 plugins have already been integrated from the most common programs used in the field. They can be used through the Scipion graphical user interface to execute and analyze typical VDS tasks. In addition, we have developed several consensus protocols that combine results from the different integrated programs to generate more robust predictions. Backstage, Scipion also facilitates the interoperability of those different software packages while tracking all of the intermediate files, parameters, and user decisions. In summary, in this article, we present Scipion-chem. This accessible, interoperable, and traceable platform provides the user with all of the tools to carry out a successful VDS workflow. Scipion-chem is openly available at https://github.com/scipion-chem.
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Affiliation(s)
- Daniel Del Hoyo
- National
Center of Biotechnology (CNB-CSIC), Madrid 28049, Spain
| | - Martin Salinas
- National
Center of Biotechnology (CNB-CSIC), Madrid 28049, Spain
| | - Alba Lomas
- National
Center of Biotechnology (CNB-CSIC), Madrid 28049, Spain
| | | | - Nuria E. Campillo
- Center
for Biological Research (CIB-CSIC), Madrid 28040, Spain
- Institute
of Mathematical Sciences (ICMAT-CSIC), Madrid 28049, Spain
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23
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Tang Y, Kim JY, Ip CKM, Bahmani A, Chen Q, Rosenberger MG, Esser-Kahn AP, Ferguson AL. Data-driven discovery of innate immunomodulators via machine learning-guided high throughput screening. Chem Sci 2023; 14:12747-12766. [PMID: 38020385 PMCID: PMC10646978 DOI: 10.1039/d3sc03613h] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
The innate immune response is vital for the success of prophylactic vaccines and immunotherapies. Control of signaling in innate immune pathways can improve prophylactic vaccines by inhibiting unfavorable systemic inflammation and immunotherapies by enhancing immune stimulation. In this work, we developed a machine learning-enabled active learning pipeline to guide in vitro experimental screening and discovery of small molecule immunomodulators that improve immune responses by altering the signaling activity of innate immune responses stimulated by traditional pattern recognition receptor agonists. Molecules were tested by in vitro high throughput screening (HTS) where we measured modulation of the nuclear factor κ-light-chain-enhancer of activated B-cells (NF-κB) and the interferon regulatory factors (IRF) pathways. These data were used to train data-driven predictive models linking molecular structure to modulation of the NF-κB and IRF responses using deep representational learning, Gaussian process regression, and Bayesian optimization. By interleaving successive rounds of model training and in vitro HTS, we performed an active learning-guided traversal of a 139 998 molecule library. After sampling only ∼2% of the library, we discovered viable molecules with unprecedented immunomodulatory capacity, including those capable of suppressing NF-κB activity by up to 15-fold, elevating NF-κB activity by up to 5-fold, and elevating IRF activity by up to 6-fold. We extracted chemical design rules identifying particular chemical fragments as principal drivers of specific immunomodulation behaviors. We validated the immunomodulatory effect of a subset of our top candidates by measuring cytokine release profiles. Of these, one molecule induced a 3-fold enhancement in IFN-β production when delivered with a cyclic di-nucleotide stimulator of interferon genes (STING) agonist. In sum, our machine learning-enabled screening approach presents an efficient immunomodulator discovery pipeline that has furnished a library of novel small molecules with a strong capacity to enhance or suppress innate immune signaling pathways to shape and improve prophylactic vaccination and immunotherapies.
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Affiliation(s)
- Yifeng Tang
- Pritzker School of Molecular Engineering, University of Chicago Chicago IL 60637 USA
| | - Jeremiah Y Kim
- Pritzker School of Molecular Engineering, University of Chicago Chicago IL 60637 USA
| | - Carman K M Ip
- Cellular Screening Center, University of Chicago Chicago IL 60637 USA
| | - Azadeh Bahmani
- Cellular Screening Center, University of Chicago Chicago IL 60637 USA
| | - Qing Chen
- Pritzker School of Molecular Engineering, University of Chicago Chicago IL 60637 USA
| | - Matthew G Rosenberger
- Pritzker School of Molecular Engineering, University of Chicago Chicago IL 60637 USA
| | - Aaron P Esser-Kahn
- Pritzker School of Molecular Engineering, University of Chicago Chicago IL 60637 USA
| | - Andrew L Ferguson
- Pritzker School of Molecular Engineering, University of Chicago Chicago IL 60637 USA
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24
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Kwon H, Ali ZA, Wong BM. Harnessing Semi-Supervised Machine Learning to Automatically Predict Bioactivities of Per- and Polyfluoroalkyl Substances (PFASs). ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2023; 10:1017-1022. [PMID: 38025956 PMCID: PMC10653214 DOI: 10.1021/acs.estlett.2c00530] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 08/23/2022] [Indexed: 12/01/2023]
Abstract
Many per- and polyfluoroalkyl substances (PFASs) pose significant health hazards due to their bioactive and persistent bioaccumulative properties. However, assessing the bioactivities of PFASs is both time-consuming and costly due to the sheer number and expense of in vivo and in vitro biological experiments. To this end, we harnessed new unsupervised/semi-supervised machine learning models to automatically predict bioactivities of PFASs in various human biological targets, including enzymes, genes, proteins, and cell lines. Our semi-supervised metric learning models were used to predict the bioactivity of PFASs found in the recent Organisation of Economic Co-operation and Development (OECD) report list, which contains 4730 PFASs used in a broad range of industries and consumers. Our work provides the first semi-supervised machine learning study of structure-activity relationships for predicting possible bioactivities in a variety of PFAS species.
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Affiliation(s)
- Hyuna Kwon
- Department
of Chemical & Environmental Engineering, University of California-Riverside, Riverside, California 92521, United States
| | - Zulfikhar A. Ali
- Department
of Physics & Astronomy, University of
California-Riverside, Riverside, California 92521, United States
| | - Bryan M. Wong
- Department
of Chemical & Environmental Engineering, University of California-Riverside, Riverside, California 92521, United States
- Department
of Physics & Astronomy, University of
California-Riverside, Riverside, California 92521, United States
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25
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Yin T, Panapitiya G, Coda ED, Saldanha EG. Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction. J Cheminform 2023; 15:105. [PMID: 37941055 PMCID: PMC10633997 DOI: 10.1186/s13321-023-00753-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 08/25/2023] [Indexed: 11/10/2023] Open
Abstract
Deep learning models have proven to be a powerful tool for the prediction of molecular properties for applications including drug design and the development of energy storage materials. However, in order to learn accurate and robust structure-property mappings, these models require large amounts of data which can be a challenge to collect given the time and resource-intensive nature of experimental material characterization efforts. Additionally, such models fail to generalize to new types of molecular structures that were not included in the model training data. The acceleration of material development through uncertainty-guided experimental design has the promise to significantly reduce the data requirements and enable faster generalization to new types of materials. To evaluate the potential of such approaches for electrolyte design applications, we perform comprehensive evaluation of existing uncertainty quantification methods on the prediction of two relevant molecular properties - aqueous solubility and redox potential. We develop novel evaluation methods to probe the utility of the uncertainty estimates for both in-domain and out-of-domain data sets. Finally, we leverage selected uncertainty estimation methods for active learning to evaluate their capacity to support experimental design.
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Affiliation(s)
- Tianzhixi Yin
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, USA.
| | - Gihan Panapitiya
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, USA
| | - Elizabeth D Coda
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, USA
- The University of California, San Diego, La Jolla, CA, USA
| | - Emily G Saldanha
- Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, USA
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26
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Moreira-Filho JT, Neves BJ, Cajas RA, Moraes JD, Andrade CH. Artificial intelligence-guided approach for efficient virtual screening of hits against Schistosoma mansoni. Future Med Chem 2023; 15:2033-2050. [PMID: 37937522 DOI: 10.4155/fmc-2023-0152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 10/06/2023] [Indexed: 11/09/2023] Open
Abstract
Background: The impact of schistosomiasis, which affects over 230 million people, emphasizes the urgency of developing new antischistosomal drugs. Artificial intelligence is vital in accelerating the drug discovery process. Methodology & results: We developed classification and regression machine learning models to predict the schistosomicidal activity of compounds not experimentally tested. The prioritized compounds were tested on schistosomula and adult stages of Schistosoma mansoni. Four compounds demonstrated significant activity against schistosomula, with 50% effective concentration values ranging from 9.8 to 32.5 μM, while exhibiting no toxicity in animal and human cell lines. Conclusion: These findings represent a significant step forward in the discovery of antischistosomal drugs. Further optimization of these active compounds can pave the way for their progression into preclinical studies.
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Affiliation(s)
- José Teófilo Moreira-Filho
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, 74605-170, Brazil
| | - Bruno Junior Neves
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, 74605-170, Brazil
| | - Rayssa Araujo Cajas
- Research Center on Neglected Diseases (NPDN), Universidade Guarulhos, Guarulhos, 07023-070, Brazil
| | - Josué de Moraes
- Research Center on Neglected Diseases (NPDN), Universidade Guarulhos, Guarulhos, 07023-070, Brazil
| | - Carolina Horta Andrade
- Laboratory of Molecular Modeling and Drug Design (LabMol), Faculdade de Farmácia, Universidade Federal de Goiás, Goiânia, 74605-170, Brazil
- Center for the Research and Advancement in Fragments and molecular Targets (CRAFT), School of Pharmaceutical Sciences at Ribeirao Preto, University of São Paulo, Ribeirão Preto, SP, Brazil
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27
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Guidotti IL, Neis A, Martinez DP, Seixas FK, Machado K, Kremer FS. Bambu and its applications in the discovery of active molecules against melanoma. J Mol Graph Model 2023; 124:108564. [PMID: 37453311 DOI: 10.1016/j.jmgm.2023.108564] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 06/14/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
PURPOSE OR OBJECTIVE Melanoma is one of the most dangerous forms of skin cancer and the discovery of novel drugs is an ongoing effort. Quantitative Structure Activity Relationship (QSAR) is a computational method that allows the estimation of the properties of a molecule, including its biological activity. QSAR models have been widely employed in the search for potential drug candidates, but also for agrochemicals and other molecules with applications in different branches of the industry. Here we present Bambu, a simple command line tool to generate QSAR models from high-throughput screening bioassays datasets. METHODS The tool was developed using the Python programming language and relies mainly on RDKit for molecule data manipulation, FLAML for automated machine learning and the PubChem REST API for data retrieval. As a proof-of-concept we have employed the tool to generate QSAR models for melanoma cell growth inhibition based on HTS data and used them to screen libraries of FDA-approved drugs and natural compounds. Additionally, Bambu was compared to QSAR-Co, another automated tool for QSAR model generation. RESULTS based on the developed tool we were able to produce QSAR models and identify a wide variety of molecules with potential melanoma cell growth inhibitors, many of which with anti-tumoral activity already described. The QSAR models are available through the URL http://caramel.ufpel.edu.br, and all data and code used to generate its models are available at Zenodo (https://doi.org/10.5281/zenodo.7495214). Bambu source code is available at GitHub (https://github.com/omixlab/bambu-v2). In the benchmark, Bambu was able to produce models with higher accuracy, recall, F1 and ROC AUC when compared to QSAR-Co for the selected datasets. CONCLUSIONS Bambu is an free and open source tool which facilitates the creation of QSAR models and can be futurely applied in a wide variety of drug discovery projects.
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Affiliation(s)
- Isadora Leitzke Guidotti
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Alessandra Neis
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Daniela Peres Martinez
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Fabiana Kömmling Seixas
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil
| | - Karina Machado
- Centro de Ciências Computacionais, Universidade Federal do Rio Grande, Rio Grande, Rio Grande do Sul, Brazil
| | - Frederico Schmitt Kremer
- Centro de Desenvolvimento Tecnológico, Universidade Federal de Pelotas, Pelotas, Rio Grande do Sul, Brazil.
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28
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Liu J, Lei X, Ji C, Pan Y. Fragment-pair based drug molecule solubility prediction through attention mechanism. Front Pharmacol 2023; 14:1255181. [PMID: 37881183 PMCID: PMC10595153 DOI: 10.3389/fphar.2023.1255181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Accepted: 09/26/2023] [Indexed: 10/27/2023] Open
Abstract
The purpose of drug discovery is to identify new drugs, and the solubility of drug molecules is an important physicochemical property in medicinal chemistry, that plays a crucial role in drug discovery. In solubility prediction, high-precision computational methods can significantly reduce the experimental costs and time associated with drug development. Therefore, artificial intelligence technologies have been widely used for solubility prediction. This study utilized the attention layer in mechanism in the deep learning model to consider the atomic-level features of the molecules, and used gated recurrent neural networks to aggregate vectors between layers. It also utilized molecular fragment technology to divide the complete molecule into pairs of fragments, extracted characteristics from each fragment pair, and finally fused the characteristics to predict the solubility of drug molecules. We compared and evaluated our method with five existing models using two performance evaluation indicators, demonstrating that our method has better performance and greater robustness.
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Affiliation(s)
- Jianping Liu
- School of Computer Science, Shaanxi Normal University, Xi’an, China
| | - Xiujuan Lei
- School of Computer Science, Shaanxi Normal University, Xi’an, China
| | - Chunyan Ji
- Computer Science Department, BNU-HKBU United International College, Zhuhai, China
| | - Yi Pan
- Faculty of Computer Science and Control Engineering, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- Shenzhen Key Laboratory of Intelligent Bioinformatics, Shenzhen Institute of Advanced Technology, Shenzhen, China
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29
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Raza A, Chohan TA, Buabeid M, Arafa ESA, Chohan TA, Fatima B, Sultana K, Ullah MS, Murtaza G. Deep learning in drug discovery: a futuristic modality to materialize the large datasets for cheminformatics. J Biomol Struct Dyn 2023; 41:9177-9192. [PMID: 36305195 DOI: 10.1080/07391102.2022.2136244] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 10/08/2022] [Indexed: 10/31/2022]
Abstract
Artificial intelligence (AI) development imitates the workings of the human brain to comprehend modern problems. The traditional approaches such as high throughput screening (HTS) and combinatorial chemistry are lengthy and expensive to the pharmaceutical industry as they can only handle a smaller dataset. Deep learning (DL) is a sophisticated AI method that uses a thorough comprehension of particular systems. The pharmaceutical industry is now adopting DL techniques to enhance the research and development process. Multi-oriented algorithms play a crucial role in the processing of QSAR analysis, de novo drug design, ADME evaluation, physicochemical analysis, preclinical development, followed by clinical trial data precision. In this study, we investigated the performance of several algorithms, including deep neural networks (DNN), convolutional neural networks (CNN) and multi-task learning (MTL), with the aim of generating high-quality, interpretable big and diverse databases for drug design and development. Studies have demonstrated that CNN, recurrent neural network and deep belief network are compatible, accurate and effective for the molecular description of pharmacodynamic properties. In Covid-19, existing pharmacological compounds has also been repurposed using DL models. In the absence of the Covid-19 vaccine, remdesivir and oseltamivir have been widely employed to treat severe SARS-CoV-2 infections. In conclusion, the results indicate the potential benefits of employing the DL strategies in the drug discovery process.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Ali Raza
- Department of pharmaceutical chemistry, Faculty of Pharmacy, The University of Lahore, Pakistan
- Institute of Molecular Biology and Biochemistry, The University of Lahore, Pakistan
| | - Talha Ali Chohan
- Institute of Molecular Biology and Biochemistry, The University of Lahore, Pakistan
- Institute of Pharmaceutical Science, UVAS, Lahore, Pakistan
| | - Manal Buabeid
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
| | - El-Shaima A Arafa
- Department of Clinical Sciences, College of Pharmacy and Health Sciences, Ajman University, Ajman, United Arab Emirates
- Centre of Medical and Bio-Allied Health Sciences Research, Ajman University, Ajman, United Arab Emirates
| | | | - Batool Fatima
- Department of biochemistry, Bahauddin Zakariya University, Multan, Pakistan
| | - Kishwar Sultana
- Department of pharmaceutical chemistry, Faculty of Pharmacy, The University of Lahore, Pakistan
| | - Malik Saad Ullah
- Department of Pharmacy, Government College University, Faisalabad, Pakistan
| | - Ghulam Murtaza
- Department of Pharmacy, COMSATS University Islamabad, Lahore Campus, Pakistan
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30
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Lee M, Min K. AmorProt: Amino Acid Molecular Fingerprints Repurposing-Based Protein Fingerprint. Biochemistry 2023; 62:2700-2709. [PMID: 37622182 DOI: 10.1021/acs.biochem.3c00253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
As protein therapeutics play an important role in almost all medical fields, numerous studies have been conducted on proteins using artificial intelligence. Artificial intelligence has enabled data-driven predictions without the need for expensive experiments. Nevertheless, unlike the various molecular fingerprint algorithms that have been developed, protein fingerprint algorithms have rarely been studied. In this study, we proposed the amino acid molecular fingerprints repurposing-based protein (AmorProt) fingerprint, a protein sequence representation method that effectively uses the molecular fingerprints corresponding to 20 amino acids. Subsequently, the performances of the tree-based machine learning and artificial neural network models were compared using (1) amyloid classification and (2) isoelectric point regression. Finally, the applicability and advantages of the developed platform were demonstrated through a case study and the following experiments: (3) comparison of dataset dependence with feature-based methods, (4) feature importance analysis, and (5) protein space analysis. Consequently, the significantly improved model performance and data-set-independent versatility of the AmorProt fingerprint were verified. The results revealed that the current protein representation method can be applied to various fields related to proteins, such as predicting their fundamental properties or interaction with ligands.
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Affiliation(s)
- Myeonghun Lee
- School of Systems Biomedical Science, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
| | - Kyoungmin Min
- School of Mechanical Engineering, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 06978, Republic of Korea
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31
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de Oliveira LHD, Cruz JN, Dos Santos CBR, de Melo EB. Multivariate QSAR, similarity search and ADMET studies based in a set of methylamine derivatives described as dopamine transporter inhibitors. Mol Divers 2023:10.1007/s11030-023-10724-5. [PMID: 37670118 DOI: 10.1007/s11030-023-10724-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 08/27/2023] [Indexed: 09/07/2023]
Abstract
The dopamine transporter (DAT), responsible for the regulation of dopaminergic neurotransmission, is implicated in the etiology of several neuropsychiatric disorders which, in turn, have contributed to high rates of disability and numerous deaths in recent years, significantly impacting the global health system. Although the research for new drugs for the treatment of neuropsychiatric disorders has evolved in recent years, the availability of DAT-selective drugs that do not generate the same psychostimulant effects observed in drugs of abuse remains scarce. Therefore, we performed a QSAR study based on a dataset of 36 methylamine derivatives described as DAT inhibitors. The model was obtained based only in descriptors derived from 2D structures, and it was validated and generated satisfactory results considering the metrics used for internal and external validation. Subsequently, a virtual screening step also based on 2D similarity was performed, where it was possible to identify a total of 1157 compounds. After a series of reductions of the set using toxicity filters, applicability domain evaluation, and pharmacokinetic properties in silico assessment, seven hit compounds were selected as the most promising to be used, in future studies, as new scaffolds for the development of new DAT inhibitors.
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Affiliation(s)
- Luiz Henrique Dias de Oliveira
- Theorical Medicinal and Environmental Chemistry Laboratory (LQMAT), Department of Pharmacy, Western Paraná State University (UNIOESTE), 2069 Universitária St., Cascavel, PR, 85819-110, Brazil
| | - Jorddy Neves Cruz
- Laboratory of Modeling and Computational Chemistry, Department of Biological and Health Sciences, Federal University of Amapá, Macapá, AP, 68902-280, Brazil
| | - Cleydson Breno Rodrigues Dos Santos
- Laboratory of Modeling and Computational Chemistry, Department of Biological and Health Sciences, Federal University of Amapá, Macapá, AP, 68902-280, Brazil
| | - Eduardo Borges de Melo
- Theorical Medicinal and Environmental Chemistry Laboratory (LQMAT), Department of Pharmacy, Western Paraná State University (UNIOESTE), 2069 Universitária St., Cascavel, PR, 85819-110, Brazil.
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Buchmann D, Schwabe M, Weiss R, Kuss AW, Schaufler K, Schlüter R, Rödiger S, Guenther S, Schultze N. Natural phenolic compounds as biofilm inhibitors of multidrug-resistant Escherichia coli - the role of similar biological processes despite structural diversity. Front Microbiol 2023; 14:1232039. [PMID: 37731930 PMCID: PMC10507321 DOI: 10.3389/fmicb.2023.1232039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Accepted: 08/08/2023] [Indexed: 09/22/2023] Open
Abstract
Multidrug-resistant gram-negative pathogens such as Escherichia coli have become increasingly difficult to treat and therefore alternative treatment options are needed. Targeting virulence factors like biofilm formation could be one such option. Inhibition of biofilm-related structures like curli and cellulose formation in E. coli has been shown for different phenolic natural compounds like epigallocatechin gallate. This study demonstrates this effect for other structurally unrelated phenolics, namely octyl gallate, scutellarein and wedelolactone. To verify whether these structurally different compounds influence identical pathways of biofilm formation in E. coli a broad comparative RNA-sequencing approach was chosen with additional RT-qPCR to gain initial insights into the pathways affected at the transcriptomic level. Bioinformatical analysis of the RNA-Seq data was performed using DESeq2, BioCyc and KEGG Mapper. The comparative bioinformatics analysis on the pathways revealed that, irrespective of their structure, all compounds mainly influenced similar biological processes. These pathways included bacterial motility, chemotaxis, biofilm formation as well as metabolic processes like arginine biosynthesis and tricarboxylic acid cycle. Overall, this work provides the first insights into the potential mechanisms of action of novel phenolic biofilm inhibitors and highlights the complex regulatory processes of biofilm formation in E. coli.
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Affiliation(s)
- David Buchmann
- Pharmaceutical Biology, Institute of Pharmacy, University of Greifswald, Greifswald, Germany
| | - Michael Schwabe
- Pharmaceutical Microbiology, Institute of Pharmacy, University of Greifswald, Greifswald, Germany
| | - Romano Weiss
- Institute of Biotechnology, Faculty Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
| | - Andreas W. Kuss
- Department of Functional Genomics, Interfaculty Institute of Genetics and Functional Genomics, University of Greifswald, Greifswald, Germany
| | - Katharina Schaufler
- Pharmaceutical Microbiology, Institute of Pharmacy, University of Greifswald, Greifswald, Germany
- Institute of Infection Medicine, Christian-Albrecht University Kiel and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Rabea Schlüter
- Imaging Center of the Department of Biology, University of Greifswald, Greifswald, Germany
| | - Stefan Rödiger
- Institute of Biotechnology, Faculty Environment and Natural Sciences, Brandenburg University of Technology Cottbus-Senftenberg, Cottbus, Germany
| | - Sebastian Guenther
- Pharmaceutical Biology, Institute of Pharmacy, University of Greifswald, Greifswald, Germany
| | - Nadin Schultze
- Pharmaceutical Biology, Institute of Pharmacy, University of Greifswald, Greifswald, Germany
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Sar S, Mitra S, Panda P, Mandal SC, Ghosh N, Halder AK, Cordeiro MNDS. In Silico Modeling and Structural Analysis of Soluble Epoxide Hydrolase Inhibitors for Enhanced Therapeutic Design. Molecules 2023; 28:6379. [PMID: 37687207 PMCID: PMC10490281 DOI: 10.3390/molecules28176379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/17/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor-ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility.
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Affiliation(s)
- Shuvam Sar
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India; (S.S.)
| | - Soumya Mitra
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India; (S.S.)
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Campus Dr. Meghnad Saha Sarani, Durgapur 713206, India
| | - Parthasarathi Panda
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Campus Dr. Meghnad Saha Sarani, Durgapur 713206, India
| | - Subhash C. Mandal
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India; (S.S.)
| | - Nilanjan Ghosh
- Department of Pharmaceutical Technology, Jadavpur University, Kolkata 700032, India; (S.S.)
| | - Amit Kumar Halder
- Dr. B. C. Roy College of Pharmacy and Allied Health Sciences, Campus Dr. Meghnad Saha Sarani, Durgapur 713206, India
- LAQV@REQUIMTE—Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Maria Natalia D. S. Cordeiro
- LAQV@REQUIMTE—Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
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Bhowmik R, Kant R, Manaithiya A, Saluja D, Vyas B, Nath R, Qureshi KA, Parkkila S, Aspatwar A. Navigating bioactivity space in anti-tubercular drug discovery through the deployment of advanced machine learning models and cheminformatics tools: a molecular modeling based retrospective study. Front Pharmacol 2023; 14:1265573. [PMID: 37705534 PMCID: PMC10495588 DOI: 10.3389/fphar.2023.1265573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2023] [Accepted: 08/10/2023] [Indexed: 09/15/2023] Open
Abstract
Mycobacterium tuberculosis is the bacterial strain that causes tuberculosis (TB). However, multidrug-resistant and extensively drug-resistant tuberculosis are significant obstacles to effective treatment. As a result, novel therapies against various strains of M. tuberculosis have been developed. Drug development is a lengthy procedure that includes identifying target protein and isolation, preclinical testing of the drug, and various phases of a clinical trial, etc., can take decades for a molecule to reach the market. Computational approaches such as QSAR, molecular docking techniques, and pharmacophore modeling have aided drug development. In this review article, we have discussed the various techniques in tuberculosis drug discovery by briefly introducing them and their importance. Also, the different databases, methods, approaches, and software used in conducting QSAR, pharmacophore modeling, and molecular docking have been discussed. The other targets targeted by these techniques in tuberculosis drug discovery have also been discussed, with important molecules discovered using these computational approaches. This review article also presents the list of drugs in a clinical trial for tuberculosis found drugs. Finally, we concluded with the challenges and future perspectives of these techniques in drug discovery.
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Affiliation(s)
- Ratul Bhowmik
- Medicinal Chemistry and Molecular Modelling Lab, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Ravi Kant
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research, Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| | - Ajay Manaithiya
- Medicinal Chemistry and Molecular Modelling Lab, Department of Pharmaceutical Chemistry, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, India
| | - Daman Saluja
- Medical Biotechnology Laboratory, Dr. B. R. Ambedkar Center for Biomedical Research, Delhi School of Public Health, IoE, University of Delhi, Delhi, India
| | - Bharti Vyas
- Department of Bioinformatics, School of Interdisciplinary Studies, Jamia Hamdard, New Delhi, India
| | - Ranajit Nath
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Siksha ‘O’ Anusandhan University, Bhubaneswar, Odisha, India
| | - Kamal A. Qureshi
- Department of Pharmaceutics, Unaizah College of Pharmacy, Qassim University, Unaizah, Al-Qassim, Saudi Arabia
| | - Seppo Parkkila
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
- Fimlab Ltd., Tampere University Hospital, Tampere, Finland
| | - Ashok Aspatwar
- Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland
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Boulos JC, Chatterjee M, Shan L, Efferth T. In Silico, In Vitro, and In Vivo Investigations on Adapalene as Repurposed Third Generation Retinoid against Multiple Myeloma and Leukemia. Cancers (Basel) 2023; 15:4136. [PMID: 37627164 PMCID: PMC10452460 DOI: 10.3390/cancers15164136] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Revised: 08/09/2023] [Accepted: 08/13/2023] [Indexed: 08/27/2023] Open
Abstract
The majority of hematopoietic cancers in adults are incurable and exhibit unpredictable remitting-relapsing patterns in response to various therapies. The proto-oncogene c-MYC has been associated with tumorigenesis, especially in hematological neoplasms. Therefore, targeting c-MYC is crucial to find effective, novel treatments for blood malignancies. To date, there are no clinically approved c-MYC inhibitors. In this study, we virtually screened 1578 Food and Drug Administration (FDA)-approved drugs from the ZINC15 database against c-MYC. The top 117 compounds from PyRx-based screening with the best binding affinities to c-MYC were subjected to molecular docking studies with AutoDock 4.2.6. Retinoids consist of synthetic and natural vitamin A derivatives. All-trans-retinoic acid (ATRA) were highly effective in hematological malignancies. In this study, adapalene, a third-generation retinoid usually used to treat acne vulgaris, was selected as a potent c-MYC inhibitor as it robustly bound to c-MYC with a lowest binding energy (LBE) of -7.27 kcal/mol, a predicted inhibition constant (pKi) of 4.69 µM, and a dissociation constant (Kd value) of 3.05 µM. Thus, we examined its impact on multiple myeloma (MM) cells in vitro and evaluated its efficiency in vivo using a xenograft tumor zebrafish model. We demonstrated that adapalene exerted substantial cytotoxicity against a panel of nine MM and two leukemic cell lines, with AMO1 cells being the most susceptible one (IC50 = 1.76 ± 0.39 µM) and, hence, the focus of this work. Adapalene (0.5 × IC50, 1 × IC50, 2 × IC50) decreased c-MYC expression and transcriptional activity in AMO1 cells in a dose-dependent manner. An examination of the cell cycle revealed that adapalene halted the cells in the G2/M phase and increased the portion of cells in the sub-G0/G1 phase after 48 and 72 h, indicating that cells failed to initiate mitosis, and consequently, cell death was triggered. Adapalene also increased the number of p-H3(Ser10) positive AMO1 cells, which is a further proof of its ability to prevent mitotic exit. Confocal imaging demonstrated that adapalene destroyed the tubulin network of U2OS cells stably transfected with a cDNA coding for α-tubulin-GFP, refraining the migration of malignant cells. Furthermore, adapalene induced DNA damage in AMO1 cells. It also induced apoptosis and autophagy, as demonstrated by flow cytometry and western blotting. Finally, adapalene impeded tumor growth in a xenograft tumor zebrafish model. In summary, the discovery of the vitamin A derivative adapalene as a c-MYC inhibitor reveals its potential as an avant-garde treatment for MM.
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Affiliation(s)
- Joelle C. Boulos
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany;
| | - Manik Chatterjee
- Translational Oncology, Comprehensive Cancer Center Mainfranken, University Hospital Würzburg, 97080 Würzburg, Germany;
| | - Letian Shan
- The First Affiliated Hospital, Zhejiang Chinese Medical University, Hangzhou 310053, China;
| | - Thomas Efferth
- Department of Pharmaceutical Biology, Institute of Pharmaceutical and Biomedical Sciences, Johannes Gutenberg University, Staudinger Weg 5, 55128 Mainz, Germany;
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Ginex T, Madruga E, Martinez A, Gil C. MBC and ECBL libraries: outstanding tools for drug discovery. Front Pharmacol 2023; 14:1244317. [PMID: 37637414 PMCID: PMC10457160 DOI: 10.3389/fphar.2023.1244317] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 08/04/2023] [Indexed: 08/29/2023] Open
Abstract
Chemical libraries have become of utmost importance to boost drug discovery processes. It is widely accepted that the quality of a chemical library depends, among others, on its availability and chemical diversity which help in rising the chances of finding good hits. In this regard, our group has developed a source for useful chemicals named Medicinal and Biological Chemistry (MBC) library. It originates from more than 30 years of experience in drug design and discovery of our research group and has successfully provided effective hits for neurological, neurodegenerative and infectious diseases. Moreover, in the last years, the European research infrastructure for chemical biology EU-OPENSCREEN has generated the European Chemical Biology library (ECBL) to be used as a source of hits for drug discovery. Here we present and discuss the updated version of the MBC library (MBC v.2022), enriched with new scaffolds and containing more than 2,500 compounds together with ECBL that collects about 100,000 small molecules. To properly address the improved potentialities of the new version of our MBC library in drug discovery, up to 44 among physicochemical and pharmaceutical properties have been calculated and compared with those of other well-known publicly available libraries. For comparison, we have used ZINC20, DrugBank, ChEMBL library, ECBL and NuBBE along with an approved drug library. Final results allowed to confirm the competitive chemical space covered by MBC v.2022 and ECBL together with suitable drug-like properties. In all, we can affirm that these two libraries represent an interesting source of new hits for drug discovery.
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Affiliation(s)
- Tiziana Ginex
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB-CSIC), Madrid, Spain
| | - Enrique Madruga
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB-CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Ana Martinez
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB-CSIC), Madrid, Spain
- Centro de Investigación Biomédica en Red en Enfermedades Neurodegenerativas (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain
| | - Carmen Gil
- Centro de Investigaciones Biológicas “Margarita Salas” (CIB-CSIC), Madrid, Spain
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37
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Tayebi L, Rahimi R, Akbarzadeh AR, Maleki A. A reliable QSPR model for predicting drug release rate from metal-organic frameworks: a simple and robust drug delivery approach. RSC Adv 2023; 13:24617-24627. [PMID: 37601598 PMCID: PMC10432896 DOI: 10.1039/d3ra00070b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 06/05/2023] [Indexed: 08/22/2023] Open
Abstract
During the drug release process, the drug is transferred from the starting point in the drug delivery system to the surface, and then to the release medium. Metal-organic frameworks (MOFs) potentially have unique features to be utilized as promising carriers for drug delivery, due to their suitable pore size, high surface area, and structural flexibility. The loading and release of various therapeutic drugs through the MOFs are effectively accomplished due to their tunable inorganic clusters and organic ligands. Since the drug release rate percentage (RES%) is a significant concern, a quantitative structure-property relationship (QSPR) method was applied to achieve an accurate model predicting the drug release rate from MOFs. Structure-based descriptors, including the number of nitrogen and oxygen atoms, along with two other adjusted descriptors, were applied for obtaining the best multilinear regression (BMLR) model. Drug release rates from 67 MOFs were applied to provide a precise model. The coefficients of determination (R2) for the training and test sets obtained were both 0.9999. The root mean square error for prediction (RMSEP) of the RES% values for the training and test sets were 0.006 and 0.005, respectively. To examine the precision of the model, external validation was performed through a set of new observations, which demonstrated that the model works to a satisfactory degree.
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Affiliation(s)
- Leila Tayebi
- Department of Chemistry, Iran University of Science and Technology P. O. Box: 16846-13114 Tehran Islamic Republic of Iran
| | - Rahmatollah Rahimi
- Department of Chemistry, Iran University of Science and Technology P. O. Box: 16846-13114 Tehran Islamic Republic of Iran
| | - Ali Reza Akbarzadeh
- Department of Chemistry, Iran University of Science and Technology P. O. Box: 16846-13114 Tehran Islamic Republic of Iran
| | - Ali Maleki
- Department of Chemistry, Iran University of Science and Technology P. O. Box: 16846-13114 Tehran Islamic Republic of Iran
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Lemos JM, Brito da Silva MF, Dos Santos Carvalho AM, Vicente Gil HP, Fiaia Costa VA, Andrade CH, Braga RC, Grellier P, Muratov EN, Charneau S, Moreira-Filho JT, Dourado Bastos IM, Neves BJ. Multitask learning-driven identification of novel antitrypanosomal compounds. Future Med Chem 2023; 15:1449-1467. [PMID: 37701989 DOI: 10.4155/fmc-2023-0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023] Open
Abstract
Background: Chagas disease and human African trypanosomiasis cause substantial death and morbidity, particularly in low- and middle-income countries, making the need for novel drugs urgent. Methodology & results: Therefore, an explainable multitask pipeline to profile the activity of compounds against three trypanosomes (Trypanosoma brucei brucei, Trypanosoma brucei rhodesiense and Trypanosoma cruzi) were created. These models successfully discovered four new experimental hits (LC-3, LC-4, LC-6 and LC-15). Among them, LC-6 showed promising results, with IC50 values ranging 0.01-0.072 μM and selectivity indices >10,000. Conclusion: These results demonstrate that the multitask protocol offers predictivity and interpretability in the virtual screening of new antitrypanosomal compounds and has the potential to improve hit rates in Chagas and human African trypanosomiasis projects.
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Affiliation(s)
- Jade Milhomem Lemos
- LabChem - Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia,74605-170, GO, Brazil
| | - Meryck Felipe Brito da Silva
- LabChem - Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia,74605-170, GO, Brazil
| | - Alexandra Maria Dos Santos Carvalho
- Pathogen-Host Interface Laboratory, Department of Cell Biology, Institute of Biological Sciences, University of Brasilia, Brasilia, 70910-900, DF, Brazil
| | - Henric Pietro Vicente Gil
- LabChem - Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia,74605-170, GO, Brazil
| | - Vinícius Alexandre Fiaia Costa
- LabChem - Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia,74605-170, GO, Brazil
| | - Carolina Horta Andrade
- LabMol - Laboratory for Molecular Modeling & Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, 74605-170, GO, Brazil
| | - Rodolpho Campos Braga
- InsilicAll Ltda, Av. Eng. Luis Carlos Berrini,1748 - Itaim Bibi, 04571-010, Sao Paulo, SP, Brazil
| | - Philippe Grellier
- UMR 7245 Molécules de Communication et Adaptation des Micro-organismes, Muséum National d'Histoire Naturelle, Équipe Parasites et Protistes Libres, Paris, 0575231, France
| | - Eugene N Muratov
- Laboratory for Molecular Modeling, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, 27599, NC, USA
- Department of Pharmaceutical Sciences, Federal University of Paraiba, Joao Pessoa, 58059-900, PB, Brazil
| | - Sébastien Charneau
- Department of Cell Biology, Laboratory of Biochemistry & Protein Chemistry, Institute of Biological Sciences, University of Brasilia, Brasilia, 70910-900, DF, Brazil
| | - José Teófilo Moreira-Filho
- LabMol - Laboratory for Molecular Modeling & Drug Design, Faculty of Pharmacy, Federal University of Goiás, Goiânia, 74605-170, GO, Brazil
| | - Izabela Marques Dourado Bastos
- Pathogen-Host Interface Laboratory, Department of Cell Biology, Institute of Biological Sciences, University of Brasilia, Brasilia, 70910-900, DF, Brazil
| | - Bruno Junior Neves
- LabChem - Laboratory of Cheminformatics, Faculty of Pharmacy, Federal University of Goiás, Goiânia,74605-170, GO, Brazil
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Niazi SK, Mariam Z. Recent Advances in Machine-Learning-Based Chemoinformatics: A Comprehensive Review. Int J Mol Sci 2023; 24:11488. [PMID: 37511247 PMCID: PMC10380192 DOI: 10.3390/ijms241411488] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 06/30/2023] [Accepted: 07/12/2023] [Indexed: 07/30/2023] Open
Abstract
In modern drug discovery, the combination of chemoinformatics and quantitative structure-activity relationship (QSAR) modeling has emerged as a formidable alliance, enabling researchers to harness the vast potential of machine learning (ML) techniques for predictive molecular design and analysis. This review delves into the fundamental aspects of chemoinformatics, elucidating the intricate nature of chemical data and the crucial role of molecular descriptors in unveiling the underlying molecular properties. Molecular descriptors, including 2D fingerprints and topological indices, in conjunction with the structure-activity relationships (SARs), are pivotal in unlocking the pathway to small-molecule drug discovery. Technical intricacies of developing robust ML-QSAR models, including feature selection, model validation, and performance evaluation, are discussed herewith. Various ML algorithms, such as regression analysis and support vector machines, are showcased in the text for their ability to predict and comprehend the relationships between molecular structures and biological activities. This review serves as a comprehensive guide for researchers, providing an understanding of the synergy between chemoinformatics, QSAR, and ML. Due to embracing these cutting-edge technologies, predictive molecular analysis holds promise for expediting the discovery of novel therapeutic agents in the pharmaceutical sciences.
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Affiliation(s)
- Sarfaraz K Niazi
- College of Pharmacy, University of Illinois, Chicago, IL 61820, USA
| | - Zamara Mariam
- Zamara Mariam, School of Interdisciplinary Engineering & Sciences (SINES), National University of Sciences & Technology (NUST), Islamabad 24090, Pakistan
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Azad I, Khan T, Ahmad N, Khan AR, Akhter Y. Updates on drug designing approach through computational strategies: a review. Future Sci OA 2023; 9:FSO862. [PMID: 37180609 PMCID: PMC10167725 DOI: 10.2144/fsoa-2022-0085] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 04/12/2023] [Indexed: 05/16/2023] Open
Abstract
The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.
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Affiliation(s)
- Iqbal Azad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Tahmeena Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Naseem Ahmad
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Abdul Rahman Khan
- Department of Chemistry, Integral University, Dasauli, P.O. Bas-ha, Kursi Road, Lucknow, 226026, UP, India
| | - Yusuf Akhter
- Department of Biotechnology, Babasaheb Bhimrao Ambedkar University, Vidya Vihar, Raebareli Road, Lucknow, UP, 2260025, India
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Mehyar N. Coronaviruses SARS-CoV, MERS-CoV, and SARS-CoV-2 helicase inhibitors: A systematic review of in vitro studies. J Virus Erad 2023:100327. [PMID: 37363132 PMCID: PMC10214743 DOI: 10.1016/j.jve.2023.100327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 05/11/2023] [Accepted: 05/22/2023] [Indexed: 06/28/2023] Open
Abstract
Introduction The recent outbreak of SARS-CoV-2 significantly increased the need to find inhibitors that target the essential enzymes for virus replication in the host cells. This systematic review was conducted to identify potential inhibitors of SARS-CoV, MERS-CoV, and SARS-CoV-2 helicases that have been tested by in vitro methods. The inhibition mechanisms of these compounds were discussed in this review, in addition to their cytotoxic and viral infection protection properties. Methods The databases PUBMED/MEDLINE, EMBASE, SCOPUS, and Web of Science were searched using different combinations of the keywords "helicase", "nsp13", "inhibitors", "coronaviridae", "coronaviruses", "virus replication", "replication", and "antagonists and inhibitors". Results By the end of this search, a total of 6854 articles had been identified. Thirty-one articles were included in this review. These studies reported the inhibitory effects of 309 compounds on SARS-CoV, MERS-CoV, and SARS-CoV-2 helicase activities measured by in vitro methods. Helicase inhibitors were categorized according to the type of coronavirus and the type of tested enzymatic activity, nature, approval, inhibition level, cytotoxicity, and viral infection protection effects. These inhibitors are classified according to the site of their interaction with the coronavirus helicases into four types: zinc-binding site inhibitors, nucleic acid binding site inhibitors, nucleotide-binding site inhibitors, and inhibitors with no clear interaction site. Conclusion Evidence from in vitro studies suggests that helicase inhibitors have a high potential as antiviral agents. Several helicase inhibitors tested in vitro showed good antiviral activities while maintaining moderate cytotoxicity. These inhibitors should be clinically investigated to determine their efficiency in treating different coronavirus infections, particularly COVID-19.
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Affiliation(s)
- Nimer Mehyar
- King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
- King Abdullah International Medical Research Center, Riyadh, Saudi Arabia
- King Abdulaziz Medical City, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
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42
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Bassani D, Moro S. Past, Present, and Future Perspectives on Computer-Aided Drug Design Methodologies. Molecules 2023; 28:molecules28093906. [PMID: 37175316 PMCID: PMC10180087 DOI: 10.3390/molecules28093906] [Citation(s) in RCA: 24] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023] Open
Abstract
The application of computational approaches in drug discovery has been consolidated in the last decades. These families of techniques are usually grouped under the common name of "computer-aided drug design" (CADD), and they now constitute one of the pillars in the pharmaceutical discovery pipelines in many academic and industrial environments. Their implementation has been demonstrated to tremendously improve the speed of the early discovery steps, allowing for the proficient and rational choice of proper compounds for a desired therapeutic need among the extreme vastness of the drug-like chemical space. Moreover, the application of CADD approaches allows the rationalization of biochemical and interactive processes of pharmaceutical interest at the molecular level. Because of this, computational tools are now extensively used also in the field of rational 3D design and optimization of chemical entities starting from the structural information of the targets, which can be experimentally resolved or can also be obtained with other computer-based techniques. In this work, we revised the state-of-the-art computer-aided drug design methods, focusing on their application in different scenarios of pharmaceutical and biological interest, not only highlighting their great potential and their benefits, but also discussing their actual limitations and eventual weaknesses. This work can be considered a brief overview of computational methods for drug discovery.
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Affiliation(s)
- Davide Bassani
- Pharmaceutical Research & Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., 4070 Basel, Switzerland
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
| | - Stefano Moro
- Molecular Modeling Section (MMS), Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Via Marzolo 5, 35131 Padova, Italy
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43
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Mahnashi MH, Koganole P, S R PK, Ashgar SS, Shaikh IA, Joshi SD, Alqahtani AS. Synthesis, Molecular Docking Study, and Biological Evaluation of New 4-(2,5-Dimethyl-1 H-pyrrol-1-yl)- N'-(2-(substituted)acetyl)benzohydrazides as Dual Enoyl ACP Reductase and DHFR Enzyme Inhibitors. Antibiotics (Basel) 2023; 12:antibiotics12040763. [PMID: 37107123 PMCID: PMC10135272 DOI: 10.3390/antibiotics12040763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/09/2023] [Accepted: 04/12/2023] [Indexed: 04/29/2023] Open
Abstract
In this study, a new series of 4-(2,5-dimethyl-1H-pyrrol-1-yl)-N'-(2-(substituted)acetyl) benzohydrazides (5a-n) were prepared and new heterocycles underwent thorough characterization and evaluation for antibacterial activity; some of them underwent further testing for in vitro inhibition of enoyl ACP reductase and DHFR enzymes. The majority of the synthesized molecules exhibited appreciable action against DHFR and enoyl ACP reductase enzymes. Some of the synthesized compounds also showed strong antibacterial and antitubercular properties. In order to determine the potential mode of action of the synthesized compounds, a molecular docking investigation was conducted. The results revealed binding interactions with both the dihydrofolate reductase and enoyl ACP reductase active sites. These molecules represent excellent future therapeutic possibilities with potential uses in the biological and medical sciences due to the compounds' pronounced docking properties and biological activity.
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Affiliation(s)
- Mater H Mahnashi
- Department of Pharmaceutical Chemistry, College of Pharmacy, Najran University, Najran 66462, Saudi Arabia
| | - Pooja Koganole
- Novel Drug Design and Discovery Laboratory, Department of Pharmaceutical Chemistry, Soniya Education Trust's College of Pharmacy, Sangolli Rayanna Nagar, Dharwad-580 002, Karnataka 580002, India
| | - Prem Kumar S R
- Novel Drug Design and Discovery Laboratory, Department of Pharmaceutical Chemistry, Soniya Education Trust's College of Pharmacy, Sangolli Rayanna Nagar, Dharwad-580 002, Karnataka 580002, India
| | - Sami S Ashgar
- Department of Microbiology, Faculty of Medicine, Umm Al-Qura University, Makkah 21955, Saudi Arabia
| | - Ibrahim Ahmed Shaikh
- Department of Pharmacology, College of Pharmacy, Najran University, Najran 66462, Saudi Arabia
| | - Shrinivas D Joshi
- Novel Drug Design and Discovery Laboratory, Department of Pharmaceutical Chemistry, Soniya Education Trust's College of Pharmacy, Sangolli Rayanna Nagar, Dharwad-580 002, Karnataka 580002, India
| | - Ali S Alqahtani
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Khalid University, Abha 62529, Saudi Arabia
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Srisongkram T, Khamtang P, Weerapreeyakul N. Prediction of KRAS G12C inhibitors using conjoint fingerprint and machine learning-based QSAR models. J Mol Graph Model 2023; 122:108466. [PMID: 37058997 DOI: 10.1016/j.jmgm.2023.108466] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/19/2023] [Accepted: 03/29/2023] [Indexed: 04/16/2023]
Abstract
Kirsten rat sarcoma virus G12C (KRASG12C) is the major protein mutation associated with non-small cell lung cancer (NSCLC) severity. Inhibiting KRASG12C is therefore one of the key therapeutic strategies for NSCLC patients. In this paper, a cost-effective data driven drug design employing machine learning-based quantitative structure-activity relationship (QSAR) analysis was built for predicting ligand affinities against KRASG12C protein. A curated and non-redundant dataset of 1033 compounds with KRASG12C inhibitory activity (pIC50) was used to build and test the models. The PubChem fingerprint, Substructure fingerprint, Substructure fingerprint count, and the conjoint fingerprint-a combination of PubChem fingerprint and Substructure fingerprint count-were used to train the models. Using comprehensive validation methods and various machine learning algorithms, the results clearly showed that the XGBoost regression (XGBoost) achieved the highest performance in term of goodness of fit, predictivity, generalizability and model robustness (R2 = 0.81, Q2CV = 0.60, Q2Ext = 0.62, R2 - Q2Ext = 0.19, R2Y-Random = 0.31 ± 0.03, Q2Y-Random = -0.09 ± 0.04). The top 13 molecular fingerprints that correlated with the predicted pIC50 values were SubFPC274 (aromatic atoms), SubFPC307 (number of chiral-centers), PubChemFP37 (≥1 Chlorine), SubFPC18 (Number of alkylarylethers), SubFPC1 (number of primary carbons), SubFPC300 (number of 1,3-tautomerizables), PubChemFP621 (N-C:C:C:N structure), PubChemFP23 (≥1 Fluorine), SubFPC2 (number of secondary carbons), SubFPC295 (number of C-ONS bonds), PubChemFP199 (≥4 6-membered rings), PubChemFP180 (≥1 nitrogen-containing 6-membered ring), and SubFPC180 (number of tertiary amine). These molecular fingerprints were virtualized and validated using molecular docking experiments. In conclusion, this conjoint fingerprint and XGBoost-QSAR model demonstrated to be useful as a high-throughput screening tool for KRASG12C inhibitor identification and drug design.
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Affiliation(s)
- Tarapong Srisongkram
- Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, 40002, Thailand.
| | | | - Natthida Weerapreeyakul
- Division of Pharmaceutical Chemistry, Faculty of Pharmaceutical Sciences, Khon Kaen University, 40002, Thailand
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45
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Bodun DS, Omoboyowa DA, Omotuyi OI, Olugbogi EA, Balogun TA, Ezeh CJ, Omirin ES. QSAR-based virtual screening of traditional Chinese medicine for the identification of mitotic kinesin Eg5 inhibitors. Comput Biol Chem 2023; 104:107865. [PMID: 37062146 DOI: 10.1016/j.compbiolchem.2023.107865] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/31/2023] [Accepted: 04/01/2023] [Indexed: 04/18/2023]
Abstract
Cell division is a crucial process for the growth and development of all living organisms. Unfortunately, uncontrolled cell division and growth is a hallmark of cancer, leading to the formation of tumors. The Human Eg5 protein, also known as the mitotic kinesin Eg5, plays a vital role in the regulation of cell division and its dysfunction has been linked to cancer development. This study aimed to identify new inhibitors of the Human Eg5 protein. Over 2000 Traditional Chinese Medicine (TCM) compounds were screened through a combination of virtual and structure-based screening methods. The top five compounds (Compounds 1-5) showed improved binding affinity to Human Eg5 compared to the standard drug Monastrol, as demonstrated by docking and MMGBSA scores, as well as interactions with key amino acids GLY 116 and GLY 118. The potential absorption and bioactivity of these compounds were also predicted through ADMET properties and a QSAR model, respectively, and showed improved results compared to the standard. Further quantum mechanics docking confirmed the better binding affinity of the lead compound, Compound 1. Our findings highlight Compound 1-5 as promising hits for inhibiting Human Eg5 and the need for experimental validation of their potential in treating cancer.
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Affiliation(s)
- Damilola S Bodun
- Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria.
| | - Damilola A Omoboyowa
- Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria
| | - Olaposi I Omotuyi
- Department of Pharmacology and Toxicology, College of Pharmacy, Afe Babalola University, Ado Ekiti, Nigeria
| | - Ezekiel A Olugbogi
- Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria
| | - Toheeb A Balogun
- Department of Biological Sciences, University of California, San Diego, San Diego, CA, United States
| | - Chiamaka J Ezeh
- Department of Biochemistry, Micheal Okpara University of Agriculture, Umudike, Abia State, Nigeria
| | - Emmanuel S Omirin
- Department of Biochemistry, Adekunle Ajasin University, Akungba-Akoko, Ondo State, Nigeria
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46
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Naidu A, Nayak SS, Lulu S S, Sundararajan V. Advances in computational frameworks in the fight against TB: The way forward. Front Pharmacol 2023; 14:1152915. [PMID: 37077815 PMCID: PMC10106641 DOI: 10.3389/fphar.2023.1152915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 03/20/2023] [Indexed: 04/05/2023] Open
Abstract
Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its "End TB" strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for-early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.
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Affiliation(s)
| | | | | | - Vino Sundararajan
- Department of Biotechnology, School of Bio Sciences and Technology, VIT University, Vellore, India
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47
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Rao SPS, Manjunatha UH, Mikolajczak S, Ashigbie PG, Diagana TT. Drug discovery for parasitic diseases: powered by technology, enabled by pharmacology, informed by clinical science. Trends Parasitol 2023; 39:260-271. [PMID: 36803572 DOI: 10.1016/j.pt.2023.01.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/19/2023] [Accepted: 01/25/2023] [Indexed: 02/22/2023]
Abstract
While prevention is a bedrock of public health, innovative therapeutics are needed to complement the armamentarium of interventions required to achieve disease control and elimination targets for neglected diseases. Extraordinary advances in drug discovery technologies have occurred over the past decades, along with accumulation of scientific knowledge and experience in pharmacological and clinical sciences that are transforming many aspects of drug R&D across disciplines. We reflect on how these advances have propelled drug discovery for parasitic infections, focusing on malaria, kinetoplastid diseases, and cryptosporidiosis. We also discuss challenges and research priorities to accelerate discovery and development of urgently needed novel antiparasitic drugs.
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Affiliation(s)
| | | | | | - Paul G Ashigbie
- Novartis Institute for Tropical Diseases, Emeryville, CA, USA.
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48
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Minh Quang N, Tran Thai H, Le Thi H, Duc Cuong N, Hien NQ, Hoang D, Ngoc VTB, Ky Minh V, Van Tat P. Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer's Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors. ACS OMEGA 2023; 8:11076-11099. [PMID: 37008140 PMCID: PMC10061515 DOI: 10.1021/acsomega.2c07934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Searching for thiosemicarbazone derivatives with the potential to inhibit acetylcholinesterase for the treatment of Alzheimer's disease (AD) is an important current goal. The QSARKPLS, QSARANN, and QSARSVR models were constructed using binary fingerprints and physicochemical (PC) descriptors of 129 thiosemicarbazone compounds screened from a database of 3791 derivatives. The R 2 and Q 2 values for the QSARKPLS, QSARANN, and QSARSVR models are greater than 0.925 and 0.713 using dendritic fingerprint (DF) and PC descriptors, respectively. The in vitro pIC50 activities of four new design-oriented compounds N1, N2, N3, and N4, from the QSARKPLS model using DFs, are consistent with the experimental results and those from the QSARANN and QSARSVR models. The designed compounds N1, N2, N3, and N4 do not violate Lipinski-5 and Veber rules using the ADME and BoiLED-Egg methods. The binding energy, kcal mol-1, of the novel compounds to the 1ACJ-PDB protein receptor of the AChE enzyme was also obtained by molecular docking and dynamics simulations consistent with those predicted from the QSARANN and QSARSVR models. New compounds N1, N2, N3, and N4 were synthesized, and the experimental in vitro pIC50 activity was determined in agreement with those obtained from in silico models. The newly synthesized thiosemicarbazones N1, N2, N3, and N4 can inhibit 1ACJ-PDB, which is predicted to be able to cross the barrier. The DFT B3LYP/def-SV(P)-ECP quantization calculation method was used to calculate E HOMO and E LUMO to account for the activities of compounds N1, N2, N3, and N4. The quantum calculation results explained are consistent with those obtained in in silico models. The successful results here may contribute to the search for new drugs for the treatment of AD.
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Affiliation(s)
- Nguyen Minh Quang
- Faculty
of Chemical Engineering, Industrial University
of Ho Chi Minh City, 12 Nguyen Van Bao, Dist. Go Vap, Ho Chi Minh 700000, Viet Nam
| | - Hoa Tran Thai
- Faculty
of Chemistry, Hue University of Sciences, Hue University, 77 Nguyen Hue, Hue City 530000, Viet Nam
| | - Hoa Le Thi
- Faculty
of Chemistry, Hue University of Sciences, Hue University, 77 Nguyen Hue, Hue City 530000, Viet Nam
| | - Nguyen Duc Cuong
- Faculty
of Chemistry, Hue University of Sciences, Hue University, 77 Nguyen Hue, Hue City 530000, Viet Nam
- School
of Hospitality and Tourism, Hue University, 22 Lam Hoang, Hue City 530000, Viet
Nam
| | - Nguyen Quoc Hien
- Vietnam
Atomic Energy Institute, 59 Ly Thuong Kiet, Dist. Hoan Kiem, Hanoi
City 100000, Viet Nam
| | - DongQuy Hoang
- Faculty
of
Materials Science and Technology, University of Science, Vietnam National University, Ho Chi Minh 700000, Viet Nam
- Vietnam
National University, Ho Chi Minh
City 700000, Viet Nam
| | - Vu Thi Bao Ngoc
- Faculty
of Chemistry and Environment, University
of Dalat, 01 Phu Dong Thien Vuong, Dalat City 660000, Viet Nam
| | - Vo Ky Minh
- Franklin
High School, 6400 Whitelock Pkwy, Elk Grove, California 95757, United States
| | - Pham Van Tat
- Department
of Sciences and Journal Management, Hoa
Sen University, 08 Nguyen Van Trang, Dist. 01, Ho Chi Minh 700000, Viet Nam
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49
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Cortat Y, Nedyalkova M, Schindler K, Kadakia P, Demirci G, Nasiri Sovari S, Crochet A, Salentinig S, Lattuada M, Steiner OM, Zobi F. Computer-Aided Drug Design and Synthesis of Rhenium Clotrimazole Antimicrobial Agents. Antibiotics (Basel) 2023; 12:antibiotics12030619. [PMID: 36978486 PMCID: PMC10044843 DOI: 10.3390/antibiotics12030619] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 03/16/2023] [Accepted: 03/17/2023] [Indexed: 03/30/2023] Open
Abstract
In the context of the global health issue caused by the growing occurrence of antimicrobial resistance (AMR), the need for novel antimicrobial agents is becoming alarming. Inorganic and organometallic complexes represent a relatively untapped source of antibiotics. Here, we report a computer-aided drug design (CADD) based on a 'scaffold-hopping' approach for the synthesis and antibacterial evaluation of fac-Re(I) tricarbonyl complexes bearing clotrimazole (ctz) as a monodentate ligand. The prepared molecules were selected following a pre-screening in silico analysis according to modification of the 2,2'-bipyridine (bpy) ligand in the coordination sphere of the complexes. CADD pointed to chiral 4,5-pinene and 5,6-pinene bipyridine derivatives as the most promising candidates. The corresponding complexes were synthesized, tested toward methicillin-sensitive and -resistant S. aureus strains, and the obtained results evaluated with regard to their binding affinity with a homology model of the S. aureus MurG enzyme. Overall, the title species revealed very similar minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values as those of the reference compound used as the scaffold in our approach. The obtained docking scores advocate the viability of 'scaffold-hopping' for de novo design, a potential strategy for more cost- and time-efficient discovery of new antibiotics.
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Affiliation(s)
- Youri Cortat
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Miroslava Nedyalkova
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Kevin Schindler
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Parth Kadakia
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Gozde Demirci
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Sara Nasiri Sovari
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Aurelien Crochet
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Stefan Salentinig
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Marco Lattuada
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
| | - Olimpia Mamula Steiner
- Haute école d'Ingénierie et d'Architecture, University of Applied Sciences Western Switzerland HES-SO, Pérolles 80, 1700 Fribourg, Switzerland
| | - Fabio Zobi
- Department of Chemistry, Fribourg University, Chemin Du Musée 9, 1700 Fribourg, Switzerland
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50
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Horne RI, Murtada MH, Huo D, Brotzakis ZF, Gregory RC, Possenti A, Chia S, Vendruscolo M. Exploration and Exploitation Approaches Based on Generative Machine Learning to Identify Potent Small Molecule Inhibitors of α-Synuclein Secondary Nucleation. J Chem Theory Comput 2023. [PMID: 36939645 PMCID: PMC10373478 DOI: 10.1021/acs.jctc.2c01303] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/21/2023]
Abstract
The high attrition rate in drug discovery pipelines is an especially pressing issue for Parkinson's disease, for which no disease-modifying drugs have yet been approved. Numerous clinical trials targeting α-synuclein aggregation have failed, at least in part due to the challenges in identifying potent compounds in preclinical investigations. To address this problem, we present a machine learning approach that combines generative modeling and reinforcement learning to identify small molecules that perturb the kinetics of aggregation in a manner that reduces the production of oligomeric species. Training data were obtained by an assay reporting on the degree of inhibition of secondary nucleation, which is the most important mechanism of α-synuclein oligomer production. This approach resulted in the identification of small molecules with high potency against secondary nucleation.
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Affiliation(s)
- Robert I Horne
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Mhd Hussein Murtada
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Donghui Huo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.,College of Life Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Z Faidon Brotzakis
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Rebecca C Gregory
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Andrea Possenti
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
| | - Sean Chia
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom.,Bioprocessing Technology Institute, Agency of Science, Technology and Research (A*STAR), Singapore 138668, Singapore
| | - Michele Vendruscolo
- Centre for Misfolding Diseases, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, United Kingdom
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