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Faris A, Cacciatore I, Alnajjar R, Hanine H, Aouidate A, Mothana RA, Alanzi AR, Elhallaoui M. Revealing innovative JAK1 and JAK3 inhibitors: a comprehensive study utilizing QSAR, 3D-Pharmacophore screening, molecular docking, molecular dynamics, and MM/GBSA analyses. Front Mol Biosci 2024; 11:1348277. [PMID: 38516192 PMCID: PMC10956358 DOI: 10.3389/fmolb.2024.1348277] [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: 12/02/2023] [Accepted: 01/17/2024] [Indexed: 03/23/2024] Open
Abstract
The heterocycle compounds, with their diverse functionalities, are particularly effective in inhibiting Janus kinases (JAKs). Therefore, it is crucial to identify the correlation between their complex structures and biological activities for the development of new drugs for the treatment of rheumatoid arthritis (RA) and cancer. In this study, a diverse set of 28 heterocyclic compounds selective for JAK1 and JAK3 was employed to construct quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR). Artificial neural network (ANN) models were employed in the development of QSAR models. The robustness and stability of the models were assessed through internal and external methodologies, including the domain of applicability (DoA). The molecular descriptors incorporated into the model exhibited a satisfactory correlation with the receptor-ligand complex structures of JAKs observed in X-ray crystallography, making the model interpretable and predictive. Furthermore, pharmacophore models ADRRR and ADHRR were designed for each JAK1 and JAK3, proving effective in discriminating between active compounds and decoys. Both models demonstrated good performance in identifying new compounds, with an ROC of 0.83 for the ADRRR model and an ROC of 0.75 for the ADHRR model. Using a pharmacophore model, the most promising compounds were selected based on their strong affinity compared to the most active compounds in the studied series each JAK1 and JAK3. Notably, the pharmacokinetic, physicochemical properties, and biological activities of the selected compounds (As compounds ZINC79189223 and ZINC66252348) were found to be consistent with their therapeutic effects in RA, owing to their non-toxic, cholinergic nature, absence of P-glycoprotein, high gastrointestinal absorption, and ability to penetrate the blood-brain barrier. Furthermore, ADMET properties were assessed, and molecular dynamics and MM/GBSA analysis revealed stability in these molecules.
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Affiliation(s)
- Abdelmoujoud Faris
- LIMAS, Department of Chemical Sciences, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Ivana Cacciatore
- Department of Pharmacy, University ‘G. d’Annunzio’ of Chieti-Pescara, Chieti, Italy
| | - Radwan Alnajjar
- CADD Unit, PharmD, Faculty of Pharmacy, Libyan International Medical University, Benghazi, Libya
| | - Hadni Hanine
- LIMAS, Department of Chemical Sciences, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
| | - Adnane Aouidate
- School of Applied Sciences of Ait Melloul, Ibn Zohr University, Fez, Morocco
| | - Ramzi A. Mothana
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Abdullah R. Alanzi
- Department of Pharmacognosy, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Menana Elhallaoui
- LIMAS, Department of Chemical Sciences, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez, Morocco
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Nguyen HD. In silico identification of novel heterocyclic compounds combats Alzheimer's disease through inhibition of butyrylcholinesterase enzymatic activity. J Biomol Struct Dyn 2023:1-21. [PMID: 37723904 DOI: 10.1080/07391102.2023.2259482] [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: 03/31/2023] [Accepted: 09/09/2023] [Indexed: 09/20/2023]
Abstract
Increasing evidence indicates that heterocyclic molecules possess properties against butyrylcholinesterase (BChE) enzymatic activity, which is a potential therapeutic target for Alzheimer's disease (AD). Thus, this study aimed to further evaluate the relationship between heterocyclic molecules and their biological activities. A dataset of 38 selective and potent heterocyclic compounds (-log[the half‑maximal inhibitory concentration (pIC50)]) values ranging from 8.02 to 10.05) was applied to construct a quantitative structure-activity relationship (QSAR) study, including Bayesian model average (BMA), artificial neural network (ANN), multiple nonlinear regression (MNLR), and multiple linear regression (MLR) models. Four models met statistical acceptance in internal and external validation. The ANN model was superior to other models in predicting the pIC50 of the outcome. The descriptors put into the models were found to be comparable with the target-ligand complex X-ray structures, making these models interpretable. Three selected molecules possess drug-like properties (pIC50 values ranged from 9.19 to 9.54). The docking score between candidates and the BChE receptor (RCSB ID 6EYF) ranged from -8.4 to -9.0 kcal/mol. Remarkably, the pharmacokinetics, biological activities, molecular dynamics, and physicochemical properties of compound 18 (C20H22N4O, pIC50 value = 9.33, oxadiazole derivative group) support its protective effects on AD treatment due to its non-toxic nature, non-carcinogen, cholinergic nature, capability to penetrate the blood-brain barrier, and high gastrointestinal absorption.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Hai Duc Nguyen
- Department of Pharmacy, College of Pharmacy, Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon, South Korea
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3
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Nguyen HD, Kim MS. Identification of promising inhibitory heterocyclic compounds against acetylcholinesterase using QSAR, ADMET, biological activity, and molecular docking. Comput Biol Chem 2023; 104:107872. [PMID: 37119698 DOI: 10.1016/j.compbiolchem.2023.107872] [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: 03/24/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 05/01/2023]
Abstract
Heterocyclic compounds exert diverse functions, especially acetylcholinesterase (AChE) inhibition. Thus, identifying the association between their detailed structures and biological activities is important to the development of novel medications for Alzheimer's disease (AD) treatment. In this study, diverse sets of 120 potent and selective heterocyclic compounds (-log[the half‑maximal inhibitory concentration] (pIC50) values ranged from 8.01 to 12.50) were used to develop quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR), multiple nonlinear regression (MNLR), Bayesian model average (BMA), and artificial neural network (ANN) models. The models' robustness and stability have been assessed using both internal and external methodology. ANN outperforms MLR, MNLR, and BMA according to external validation. The molecular descriptors incorporated into the model were in satisfactory correlation with the AChE receptor-ligand complex X-ray structures, making the model interpretable and predictive. Three selected compounds exert drug-like characteristics (pIC50 values ranged from 11.01 to 11.17). The binding affinity between the optimal compounds and the AChE receptor (RCSB ID 3LII) ranged from - 7.4 to - 8.8 kcal/mol. Remarkably, the pharmacokinetics, physicochemical properties, and biological activities of compound 25 (C23H32N2O2, PubChem CID 118727071, pIC50 value = 11.17) were found to be consistent with its therapeutic effects in AD due to its cholinergic and non-toxic nature, non-P-glycoprotein, high gastrointestinal absorption, and capability to penetrate the blood-brain barrier.
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Affiliation(s)
- Hai Duc Nguyen
- Department of Pharmacy, College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon 57922, Republic of Korea.
| | - Min-Sun Kim
- Department of Pharmacy, College of Pharmacy and Research Institute of Life and Pharmaceutical Sciences, Sunchon National University, Suncheon 57922, Republic of Korea.
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ER-RAJY M, EL FADILI M, MRABTI NN, ZAROUGUI S, ELHALLAOUI M. QSAR, molecular docking, ADMET properties in silico studies for a series of 7-propanamide benzoxaboroles as potent anti-cancer agents. CHINESE JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1016/j.cjac.2022.100163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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5
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The Extremal Structures of r-Uniform Unicyclic Hypergraphs on the Signless Laplacian Estrada Index. MATHEMATICS 2022. [DOI: 10.3390/math10060941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
SLEE has various applications in a large variety of problems. The signless Laplacian Estrada index of a hypergraph H is defined as SLEE(H)=∑i=1neλi(Q), where λ1(Q),λ2(Q),…,λn(Q) are the eigenvalues of the signless Laplacian matrix of H. In this paper, we characterize the unique r-uniform unicyclic hypergraphs with maximum and minimum SLEE.
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Zhu J, Zhou Y, Huang J, Zhou A, Chen Z. Noninvasive Blood Glucose Concentration Measurement Based on Conservation of Energy Metabolism and Machine Learning. SENSORS 2021; 21:s21216989. [PMID: 34770294 PMCID: PMC8588061 DOI: 10.3390/s21216989] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 10/14/2021] [Accepted: 10/18/2021] [Indexed: 11/30/2022]
Abstract
Blood glucose (BG) concentration monitoring is essential for controlling complications arising from diabetes, as well as digital management of the disease. At present, finger-prick glucometers are widely used to measure BG concentrations. In consideration of the challenges of invasive BG concentration measurements involving pain, risk of infection, expense, and inconvenience, we propose a noninvasive BG concentration detection method based on the conservation of energy metabolism. In this study, a multisensor integrated detection probe was designed and manufactured by 3D-printing technology to be worn on the wrist. Two machine-learning algorithms were also applied to establish the regression model for predicting BG concentrations. The results showed that the back-propagation neural network model produced better performance than the multivariate polynomial regression model, with a mean absolute relative difference and correlation coefficient of 5.453% and 0.936, respectively. Here, about 98.413% of the predicted values were within zone A of the Clarke error grid. The above results proved the potential of our method and device for noninvasive glucose concentration detection from the human wrist.
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Affiliation(s)
- Jianming Zhu
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; (J.Z.); (Y.Z.); (A.Z.)
| | - Yu Zhou
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; (J.Z.); (Y.Z.); (A.Z.)
| | - Junxiang Huang
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China;
| | - Aojie Zhou
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; (J.Z.); (Y.Z.); (A.Z.)
| | - Zhencheng Chen
- School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China;
- Correspondence:
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Kleandrova VV, Scotti MT, Speck-Planche A. Indirect-Acting Pan-Antivirals vs. Respiratory Viruses: A Fresh Perspective on Computational Multi-Target Drug Discovery. Curr Top Med Chem 2021; 21:2687-2693. [PMID: 34636311 DOI: 10.2174/1568026621666211012110819] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 09/23/2021] [Accepted: 09/23/2021] [Indexed: 12/22/2022]
Abstract
Respiratory viruses continue to afflict mankind. Among them, pathogens such as coronaviruses [including the current pandemic agent known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)] and the one causing influenza A (IAV) are highly contagious and deadly. These can evade the immune system defenses while causing a hyperinflammatory response that can damage different tissues/organs. Simultaneously targeting immunomodulatory proteins is a plausible antiviral strategy since it could lead to the discovery of indirect-acting pan-antiviral (IAPA) agents for the treatment of diseases caused by respiratory viruses. In this context, computational approaches, which are an essential part of the modern drug discovery campaigns, could accelerate the identification of multi-target immunomodulators. This perspective discusses the usefulness of computational multi-target drug discovery for the virtual screening (drug repurposing) of IAPA agents capable of boosting the immune system through the activation of the toll-like receptor 7 (TLR7) and/or the stimulator of interferon genes (STING) while inhibiting key pro-inflammatory proteins, such as caspase-1 and tumor necrosis factor-alpha (TNF-α).
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe shosse 11, 125080, Moscow. Russian Federation
| | - Marcus T Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, 58051-900, João Pessoa. Brazil
| | - Alejandro Speck-Planche
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, 58051-900, João Pessoa. Brazil
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8
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Kleandrova VV, Speck-Planche A. The QSAR Paradigm in Fragment-Based Drug Discovery: From the Virtual Generation of Target Inhibitors to Multi-Scale Modeling. Mini Rev Med Chem 2021; 20:1357-1374. [PMID: 32013845 DOI: 10.2174/1389557520666200204123156] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 10/21/2019] [Accepted: 10/28/2019] [Indexed: 12/24/2022]
Abstract
Fragment-Based Drug Design (FBDD) has established itself as a promising approach in modern drug discovery, accelerating and improving lead optimization, while playing a crucial role in diminishing the high attrition rates at all stages in the drug development process. On the other hand, FBDD has benefited from the application of computational methodologies, where the models derived from the Quantitative Structure-Activity Relationships (QSAR) have become consolidated tools. This mini-review focuses on the evolution and main applications of the QSAR paradigm in the context of FBDD in the last five years. This report places particular emphasis on the QSAR models derived from fragment-based topological approaches to extract physicochemical and/or structural information, allowing to design potentially novel mono- or multi-target inhibitors from relatively large and heterogeneous databases. Here, we also discuss the need to apply multi-scale modeling, to exemplify how different datasets based on target inhibition can be simultaneously integrated and predicted together with other relevant endpoints such as the biological activity against non-biomolecular targets, as well as in vitro and in vivo toxicity and pharmacokinetic properties. In this context, seminal papers are briefly analyzed. As huge amounts of data continue to accumulate in the domains of the chemical, biological and biomedical sciences, it has become clear that drug discovery must be viewed as a multi-scale optimization process. An ideal multi-scale approach should integrate diverse chemical and biological data and also serve as a knowledge generator, enabling the design of potentially optimal chemicals that may become therapeutic agents.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Volokolamskoe Shosse 11, 125080, Moscow, Russian Federation
| | - Alejandro Speck-Planche
- Department of Chemistry, Institute of Pharmacy, I.M. Sechenov First Moscow State Medical University, Trubetskaya Str., 8, b. 2, 119992, Moscow, Russian Federation
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9
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Daoui O, Elkhattabi S, Chtita S, Elkhalabi R, Zgou H, Benjelloun AT. QSAR, molecular docking and ADMET properties in silico studies of novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2-Yl derivatives derived from dimedone as potent anti-tumor agents through inhibition of C-Met receptor tyrosine kinase. Heliyon 2021; 7:e07463. [PMID: 34296007 PMCID: PMC8282965 DOI: 10.1016/j.heliyon.2021.e07463] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 06/14/2021] [Accepted: 06/29/2021] [Indexed: 02/09/2023] Open
Abstract
A quantitative structure-activity relationship (QSAR) study is performed on 48 novel 4,5,6,7-tetrahydrobenzo[D]-thiazol-2 derivatives as anticancer agents capable of inhibiting c-Met receptor tyrosine kinase. The present study is conducted using multiple linear regression, multiple nonlinear regression and artificial neural networks. Three QSAR models are developed after partitioning the database into two sets (training and test) via the k-means method. The obtained values of the correlation coefficients by the three developed QSAR models are 0.90, 0.91 and 0.92, respectively. The resulting models are validated by using the external validation, leave-one-out cross-validation, Y-randomization test, and applicability domain methods. Moreover, we evaluated the drug-likeness properties of seven selected molecules based on their observed high activity to inhibit the c-Met receptor. The results of the evaluation showed that three of the seven compounds present drug-like characteristics. In order to identify the important active sites for the inhibition of the c-Met receptor responsible for the development of cancer cell lines, the crystallized form of the Crizotinib-c-Met complex (PDB code: 2WGJ) is used. These sites are used as references in the molecular docking test of the three selected molecules to identify the most suitable molecule for use as a new c-Met inhibitor. A comparative study is conducted based on the evaluation of the predicted properties of ADMET in silico between the candidate molecule and the Crizotinib inhibitor. The comparison results show that the selected molecule can be used as new anticancer drug candidates.
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Affiliation(s)
- Ossama Daoui
- Laboratory of Engineering, Systems and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
| | - Souad Elkhattabi
- Laboratory of Engineering, Systems and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
| | - Samir Chtita
- Laboratory of Physical Chemistry of Materials, Faculty of Sciences Ben M'Sik, Hassan II University of Casablanca, Casablanca P.O. Box 7955, Morocco
| | - Rachida Elkhalabi
- Laboratory of Applied Organic Chemistry, Faculty of Sciences and Technologies, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco
| | - Hsaine Zgou
- Polydisciplinary Faculty of Ouarzazate, Ibn Zohr University, Agadir, Morocco
| | - Adil Touimi Benjelloun
- LIMAS, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdallah University, Fez, Morocco
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Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov 2021; 16:949-959. [PMID: 33779453 DOI: 10.1080/17460441.2021.1909567] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Introduction: Artificial intelligence (AI) has inspired computer-aided drug discovery. The widespread adoption of machine learning, in particular deep learning, in multiple scientific disciplines, and the advances in computing hardware and software, among other factors, continue to fuel this development. Much of the initial skepticism regarding applications of AI in pharmaceutical discovery has started to vanish, consequently benefitting medicinal chemistry.Areas covered: The current status of AI in chemoinformatics is reviewed. The topics discussed herein include quantitative structure-activity/property relationship and structure-based modeling, de novo molecular design, and chemical synthesis prediction. Advantages and limitations of current deep learning applications are highlighted, together with a perspective on next-generation AI for drug discovery.Expert opinion: Deep learning-based approaches have only begun to address some fundamental problems in drug discovery. Certain methodological advances, such as message-passing models, spatial-symmetry-preserving networks, hybrid de novo design, and other innovative machine learning paradigms, will likely become commonplace and help address some of the most challenging questions. Open data sharing and model development will play a central role in the advancement of drug discovery with AI.
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Affiliation(s)
- José Jiménez-Luna
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Francesca Grisoni
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
| | - Nils Weskamp
- Boehringer Ingelheim Pharma GmbH & Co. KG, Biberach an Der Riss, Germany
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland
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Kleandrova VV, Scotti L, Bezerra Mendonça Junior FJ, Muratov E, Scotti MT, Speck-Planche A. QSAR Modeling for Multi-Target Drug Discovery: Designing Simultaneous Inhibitors of Proteins in Diverse Pathogenic Parasites. Front Chem 2021; 9:634663. [PMID: 33777898 PMCID: PMC7987820 DOI: 10.3389/fchem.2021.634663] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2020] [Accepted: 01/22/2021] [Indexed: 11/21/2022] Open
Abstract
Parasitic diseases remain as unresolved health issues worldwide. While for some parasites the treatments involve drug combinations with serious side effects, for others, chemical therapies are inefficient due to the emergence of drug resistance. This urges the search for novel antiparasitic agents able to act through multiple mechanisms of action. Here, we report the first multi-target model based on quantitative structure-activity relationships and a multilayer perceptron neural network (mt-QSAR-MLP) to virtually design and predict versatile inhibitors of proteins involved in the survival and/or infectivity of different pathogenic parasites. The mt-QSAR-MLP model exhibited high accuracy (>80%) in both training and test sets for the classification/prediction of protein inhibitors. Several fragments were directly extracted from the physicochemical and structural interpretations of the molecular descriptors in the mt-QSAR-MLP model. Such interpretations enabled the generation of four molecules that were predicted as multi-target inhibitors against at least three of the five parasitic proteins reported here with two of the molecules being predicted to inhibit all the proteins. Docking calculations converged with the mt-QSAR-MLP model regarding the multi-target profile of the designed molecules. The designed molecules exhibited drug-like properties, complying with Lipinski’s rule of five, as well as Ghose’s filter and Veber’s guidelines.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Moscow State University of Food Production, Moscow, Russian Federation
| | - Luciana Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, Brazil
| | | | - Eugene Muratov
- Laboratory for Molecular Modeling, The UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Marcus T Scotti
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, Brazil
| | - Alejandro Speck-Planche
- Postgraduate Program in Natural and Synthetic Bioactive Products, Federal University of Paraíba, João Pessoa, Brazil
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Diallo BN, Swart T, Hoppe HC, Tastan Bishop Ö, Lobb K. Potential repurposing of four FDA approved compounds with antiplasmodial activity identified through proteome scale computational drug discovery and in vitro assay. Sci Rep 2021; 11:1413. [PMID: 33446838 PMCID: PMC7809352 DOI: 10.1038/s41598-020-80722-2] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Accepted: 12/01/2020] [Indexed: 12/14/2022] Open
Abstract
Malaria elimination can benefit from time and cost-efficient approaches for antimalarials such as drug repurposing. In this work, 796 DrugBank compounds were screened against 36 Plasmodium falciparum targets using QuickVina-W. Hits were selected after rescoring using GRaph Interaction Matching (GRIM) and ligand efficiency metrics: surface efficiency index (SEI), binding efficiency index (BEI) and lipophilic efficiency (LipE). They were further evaluated in Molecular dynamics (MD). Twenty-five protein-ligand complexes were finally retained from the 28,656 (36 × 796) dockings. Hit GRIM scores (0.58 to 0.78) showed their molecular interaction similarity to co-crystallized ligands. Minimum LipE (3), SEI (23) and BEI (7) were in at least acceptable thresholds for hits. Binding energies ranged from -6 to -11 kcal/mol. Ligands showed stability in MD simulation with good hydrogen bonding and favorable protein-ligand interactions energy (the poorest being -140.12 kcal/mol). In vitro testing showed 4 active compounds with two having IC50 values in the single-digit μM range.
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Affiliation(s)
- Bakary N'tji Diallo
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Tarryn Swart
- Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Heinrich C Hoppe
- Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Özlem Tastan Bishop
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa
| | - Kevin Lobb
- Research Unit in Bioinformatics (RUBi), Department of Biochemistry and Microbiology, Rhodes University, Grahamstown, 6140, South Africa.
- Department of Chemistry, Rhodes University, Grahamstown, 6140, South Africa.
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Upadhyay HC, Singh M, Prakash O, Khan F, Srivastava SK, Bawankule DU. QSAR, ADME and docking guided semi-synthesis and in vitro evaluation of 4-hydroxy-α-tetralone analogs for anti-inflammatory activity. SN APPLIED SCIENCES 2020. [DOI: 10.1007/s42452-020-03798-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
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14
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García-García A, Echenique-Errandonea E, Cepeda J, Choquesillo-Lazarte D, Seco JM, Salinas-Castillo A, Sánchez-Moreno M, Méndez-Arriaga JM, Rodríguez-Diéguez A. Photoluminescence and in vitro cytotoxicity analysis in a novel mononuclear Zn(II) coordination compound based on bumetanide. Inorganica Chim Acta 2020. [DOI: 10.1016/j.ica.2020.119708] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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15
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Mansbach RA, Leus IV, Mehla J, Lopez CA, Walker JK, Rybenkov VV, Hengartner NW, Zgurskaya HI, Gnanakaran S. Machine Learning Algorithm Identifies an Antibiotic Vocabulary for Permeating Gram-Negative Bacteria. J Chem Inf Model 2020; 60:2838-2847. [PMID: 32453589 DOI: 10.1021/acs.jcim.0c00352] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Drug discovery faces a crisis. The industry has used up the "obvious" space in which to find novel drugs for biomedical applications, and productivity is declining. One strategy to combat this is rational approaches to expand the search space without relying on chemical intuition, to avoid rediscovery of similar spaces. In this work, we present proof of concept of an approach to rationally identify a "chemical vocabulary" related to a specific drug activity of interest without employing known rules. We focus on the pressing concern of multidrug resistance in Pseudomonas aeruginosa by searching for submolecules that promote compound entry into this bacterium. By synergizing theory, computation, and experiment, we validate our approach, explain the molecular mechanism behind identified fragments promoting compound entry, and select candidate compounds from an external library that display good permeation ability.
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Affiliation(s)
- Rachael A Mansbach
- Department of Theoretical Biology and Biophysics, Los Alamos National Lab, MS-K710, P.O. Box 1663, Los Alamos, New Mexico 87545-0001, United States
| | - Inga V Leus
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, SLSRC, Rm 1000, Norman, Oklahoma 73019-5251, United States
| | - Jitender Mehla
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, SLSRC, Rm 1000, Norman, Oklahoma 73019-5251, United States
| | - Cesar A Lopez
- Department of Theoretical Biology and Biophysics, Los Alamos National Lab, MS-K710, P.O. Box 1663, Los Alamos, New Mexico 87545-0001, United States
| | - John K Walker
- Pharmacology and Physiological Science, School of Medicine, Saint Louis University, Schwitalla Hall, Room M362, St. Louis, Missouri 63104, United States
| | - Valentin V Rybenkov
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, SLSRC, Rm 1000, Norman, Oklahoma 73019-5251, United States
| | - Nicolas W Hengartner
- Department of Theoretical Biology and Biophysics, Los Alamos National Lab, MS-K710, P.O. Box 1663, Los Alamos, New Mexico 87545-0001, United States
| | - Helen I Zgurskaya
- Department of Chemistry and Biochemistry, University of Oklahoma, 101 Stephenson Parkway, SLSRC, Rm 1000, Norman, Oklahoma 73019-5251, United States
| | - S Gnanakaran
- Department of Theoretical Biology and Biophysics, Los Alamos National Lab, MS-K710, P.O. Box 1663, Los Alamos, New Mexico 87545-0001, United States
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16
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Halder AK, Dias Soeiro Cordeiro MN. Advanced in Silico Methods for the Development of Anti- Leishmaniasis and Anti-Trypanosomiasis Agents. Curr Med Chem 2020; 27:697-718. [DOI: 10.2174/0929867325666181031093702] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 07/24/2018] [Accepted: 09/19/2018] [Indexed: 11/22/2022]
Abstract
Leishmaniasis and trypanosomiasis occur primarily in undeveloped countries and account
for millions of deaths and disability-adjusted life years. Limited therapeutic options, high toxicity of
chemotherapeutic drugs and the emergence of drug resistance associated with these diseases demand
urgent development of novel therapeutic agents for the treatment of these dreadful diseases. In the last
decades, different in silico methods have been successfully implemented for supporting the lengthy and
expensive drug discovery process. In the current review, we discuss recent advances pertaining to in
silico analyses towards lead identification, lead modification and target identification of antileishmaniasis
and anti-trypanosomiasis agents. We describe recent applications of some important in
silico approaches, such as 2D-QSAR, 3D-QSAR, pharmacophore mapping, molecular docking, and so
forth, with the aim of understanding the utility of these techniques for the design of novel therapeutic
anti-parasitic agents. This review focuses on: (a) advanced computational drug design options; (b) diverse
methodologies - e.g.: use of machine learning tools, software solutions, and web-platforms; (c)
recent applications and advances in the last five years; (d) experimental validations of in silico predictions;
(e) virtual screening tools; and (f) rationale or justification for the selection of these in silico
methods.
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Affiliation(s)
- Amit Kumar Halder
- LAQV@ REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, Porto 4169-007, Portugal
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17
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Toropov AA, Toropova AP. QSPR/QSAR: State-of-Art, Weirdness, the Future. Molecules 2020; 25:E1292. [PMID: 32178379 PMCID: PMC7143984 DOI: 10.3390/molecules25061292] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 03/06/2020] [Accepted: 03/10/2020] [Indexed: 12/15/2022] Open
Abstract
Ability of quantitative structure-property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the "statistical similarity" of different endpoints is suggested and illustrated by an example for relatively "distanced each from other" endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood-brain barrier.
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Affiliation(s)
| | - Alla P. Toropova
- Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Science, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via Mario Negri 2, 20156 Milano, Italy;
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18
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Liu M, Quinn RJ. Fragment-based screening with natural products for novel anti-parasitic disease drug discovery. Expert Opin Drug Discov 2019; 14:1283-1295. [PMID: 31512943 PMCID: PMC6816479 DOI: 10.1080/17460441.2019.1653849] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2019] [Accepted: 08/06/2019] [Indexed: 12/30/2022]
Abstract
Introduction: Fragment-based drug discovery can identify relatively simple compounds with low binding affinity due to fewer binding interactions with protein targets. FBDD reduces the library size and provides simpler starting points for subsequent chemical optimization of initial hits. A much greater proportion of chemical space can be sampled in fragment-based screening compared to larger molecules with typical molecular weights (MWs) of 250-500 g mol-1 used in high-throughput screening (HTS) libraries. Areas covered: The authors cover the role of natural products in fragment-based drug discovery against parasitic disease targets. They review the approaches to develop fragment-based libraries either using natural products or natural product-like compounds. The authors present approaches to fragment-based drug discovery against parasitic diseases and compare these libraries with the 3D attributes of natural products. Expert opinion: To effectively use the three-dimensional properties and the chemical diversity of natural products in fragment-based drug discovery against parasitic diseases, there needs to be a mind-shift. Library design, in the medicinal chemistry area, has acknowledged that escaping flat-land is very important to increase the chances of clinical success. Attempts to increase sp3 richness in fragment libraries are acknowledged. Sufficient low molecular weight natural products are known to create true natural product fragment libraries.
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Affiliation(s)
- Miaomiao Liu
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Australia
| | - Ronald J. Quinn
- Griffith Institute for Drug Discovery, Griffith University, Brisbane, Australia
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19
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Braga SS. Multi-target drugs active against leishmaniasis: A paradigm of drug repurposing. Eur J Med Chem 2019; 183:111660. [PMID: 31514064 DOI: 10.1016/j.ejmech.2019.111660] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 08/27/2019] [Accepted: 08/28/2019] [Indexed: 11/17/2022]
Abstract
This mini-review focuses on leishmanicidal drugs that were sourced from small molecules previously approved for other diseases. The mechanisms of action of these molecules are herein explored, to probe the origins of their inter-species growth inhibitory activities. It is shown how the transversal action of the azoles - fluconazole, posaconazole and itraconazole - in both fungi and Leishmania is due to the occurrence of the same target, lanosterol 14-α-demethylase, in these two groups of species. In turn, the drugs miltefosine and amphotericin B are presented as truly multi-target agents, acting on small molecules, proteins, genes and even organelles. Steps towards future leishmanicidal drug candidates based on the multi-target strategy and on drug repurposing are also briefly presented.
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Affiliation(s)
- Susana Santos Braga
- QOPNA & LAQV/REQUIMTE, Department of Chemistry, University of Aveiro, 3810-193, Aveiro, Portugal.
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20
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Speck-Planche A. Combining Ensemble Learning with a Fragment-Based Topological Approach To Generate New Molecular Diversity in Drug Discovery: In Silico Design of Hsp90 Inhibitors. ACS OMEGA 2018; 3:14704-14716. [PMID: 30555986 PMCID: PMC6289491 DOI: 10.1021/acsomega.8b02419] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/18/2018] [Accepted: 10/23/2018] [Indexed: 05/05/2023]
Abstract
Machine learning methods have revolutionized modern science, providing fast and accurate solutions to multiple problems. However, they are commonly treated as "black boxes". Therefore, in important scientific fields such as medicinal chemistry and drug discovery, machine learning methods are restricted almost exclusively to the task of performing predictions of large and heterogeneous data sets of chemicals. The lack of interpretability prevents the full exploitation of the machine learning models as generators of new chemical knowledge. This work focuses on the development of an ensemble learning model for the prediction and design of potent dual heat shock protein 90 (Hsp90) inhibitors. The model displays accuracy higher than 80% in both training and test sets. To use the ensemble model as a generator of new chemical knowledge, three steps were followed. First, a physicochemical and/or structural interpretation was provided for each molecular descriptor present in the ensemble learning model. Second, the term "pseudolinear equation" was introduced within the context of machine learning to calculate the relative quantitative contributions of different molecular fragments to the inhibitory activity against the two Hsp90 isoforms studied here. Finally, by assembling the fragments with positive contributions, new molecules were designed, being predicted as potent Hsp90 inhibitors. According to Lipinski's rule of five, the designed molecules were found to exhibit potentially good oral bioavailability, a primordial property that chemicals must have to pass early stages in drug discovery. The present approach based on the combination of ensemble learning and fragment-based topological design holds great promise in drug discovery, and it can be adapted and applied to many different scientific disciplines.
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21
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BET bromodomain inhibitors: fragment-based in silico design using multi-target QSAR models. Mol Divers 2018; 23:555-572. [PMID: 30421269 DOI: 10.1007/s11030-018-9890-8] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2018] [Accepted: 10/30/2018] [Indexed: 12/17/2022]
Abstract
Epigenetics has become a focus of interest in drug discovery. In this sense, bromodomain-containing proteins have emerged as potential epigenetic targets in cancer research and other therapeutic areas. Several computational approaches have been applied to the prediction of bromodomain inhibitors. Nevertheless, such approaches have several drawbacks such as the fact that they predict activity against only one bromodomain-containing protein, using structurally related compounds. Also, there are no reports focused on meaningfully analyzing the physicochemical/structural features that are necessary for the design of a bromodomain inhibitor. This work describes the development of two different multi-target models based on quantitative structure-activity relationships (mt-QSAR) for the prediction and in silico design of multi-target bromodomain inhibitors against the proteins BRD2, BRD3, and BRD4. The first model relied on linear discriminant analysis (LDA) while the second focused on artificial neural networks. Both models exhibited accuracies higher than 85% in the dataset. Several molecular fragments were extracted, and their contributions to the inhibitory activity against the three BET proteins were calculated by the LDA model. Six molecules were designed by assembling the fragments with positive contributions, and they were predicted as multi-target BET bromodomain inhibitors by the two mt-QSAR models. Molecular docking calculations converged with the predictions performed by the mt-QSAR models, suggesting that the designed molecules can exhibit potent activity against the three BET proteins. These molecules complied with the Lipinski's rule of five.
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22
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Barreiro E, Munteanu CR, Cruz-Monteagudo M, Pazos A, González-Díaz H. Net-Net Auto Machine Learning (AutoML) Prediction of Complex Ecosystems. Sci Rep 2018; 8:12340. [PMID: 30120369 PMCID: PMC6098100 DOI: 10.1038/s41598-018-30637-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Accepted: 07/24/2018] [Indexed: 11/09/2022] Open
Abstract
Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume of information. Consequently, computational prediction becomes an important goal. Artificial Neural Networks (ANNs) are Machine Learning (ML) algorithms that may be used to predict BENs, using as input Shannon entropy information measures (Shk) of known ecosystems to train them. However, it is difficult to select a priori which ANN topology will have a higher accuracy. Interestingly, Auto Machine Learning (AutoML) methods focus on the automatic selection of the more efficient ML algorithms for specific problems. In this work, a preliminary study of a new approach to AutoML selection of ANNs is proposed for the prediction of BENs. We call it the Net-Net AutoML approach, because it uses for the first time Shk values of both networks involving BENs (networks to be predicted) and ANN topologies (networks to be tested). Twelve types of classifiers have been tested for the Net-Net model including linear, Bayesian, trees-based methods, multilayer perceptrons and deep neuronal networks. The best Net-Net AutoML model for 338,050 outputs of 10 ANN topologies for links of 69 BENs was obtained with a deep fully connected neuronal network, characterized by a test accuracy of 0.866 and a test AUROC of 0.935. This work paves the way for the application of Net-Net AutoML to other systems or ML algorithms.
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Affiliation(s)
- Enrique Barreiro
- Department of Computation, Computer Science Faculty, University of A Coruna (UDC), 15071, A Coruña, Spain.,Center for Computational Science (CCS), University of Miami (UM), Miami, 33136, FL, USA.,West Coast University, Miami Campus, 33178, FL, USA
| | - Cristian R Munteanu
- Department of Computation, Computer Science Faculty, University of A Coruna (UDC), 15071, A Coruña, Spain
| | - Maykel Cruz-Monteagudo
- Center for Computational Science (CCS), University of Miami (UM), Miami, 33136, FL, USA.,West Coast University, Miami Campus, 33178, FL, USA
| | - Alejandro Pazos
- Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), A Coruña, 15006, Spain
| | - Humbert González-Díaz
- Faculty of Science and Technology, University of the Basque Country (UPV/EHU), 48940, Biscay, Spain. .,IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Biscay, Spain.
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23
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Baheti K, Kale M. Methodologies Related to Computational Models in View of Developing Anti-Alzheimer Drugs: An Overview. Curr Drug Discov Technol 2018; 16:66-73. [PMID: 29663890 DOI: 10.2174/1570163815666180417120833] [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: 07/27/2017] [Revised: 04/06/2018] [Accepted: 04/13/2018] [Indexed: 11/22/2022]
Abstract
BACKGROUND Since the last two decades, there has been more focus on the development strategies related to Anti-Alzheimer's drug research. This may be attributed to the fact that most of the Alzheimer's cases are still mostly unknown except for a few cases, where genetic differences have been identified. With the progress of the disease, the symptoms involve intellectual deterioration, memory impairment, abnormal personality and behavioural patterns, confusion, aggression, mood swings, irritability Current therapies available for this disease give only symptomatic relief and do not focus on manipulations of biololecular processes. METHODS Nearly all the therapies to treat Alzheimer's disease, target to change the amyloid cascade which is considered to be important in AD pathogenesis. New drug regimens are not able to keep pace with the ever-increasing understanding about dementia at the molecular level. Looking into these aggravated problems, we thought to put forth molecular modeling as a drug discovery approach for developing novel drugs to treat Alzheimer disease. The disease is incurable and it gets worst as it advances and finally causes death. Due to this, the design of drugs to treat this disease has become an utmost priority for research. One of the most important emerging technologies applied for this has been Computer-assisted drug design (CADD). It is a research tool that employs large-scale computing strategies in an attempt to develop a model receptor site which can be used for designing of an anti-Alzheimer drug. RESULTS Various models of amyloid-based calcium channels have been computationally optimized. Docking and De novo evolution are used to design the compounds. They are further subjected to absorption, distribution, metabolism, excretion and toxicity (ADMET) studies to finally bring about active compounds that are able to cross BBB. Many novel compounds have been designed which might be promising ones for the treatment of AD. CONCLUSION The present review describes the research carried out on various heterocyclic scaffolds that can serve as lead compounds to design Anti-Alzheimer's drugs in the future. The molecular modeling methods can thus become a better alternative for the discovery of newer Anti- Alzheimer agents. This methodology is extremely useful to design drugs in minimum time with enhanced activity keeping balanced ethical considerations. Thus, the researchers are opting for this improved process over the conventional methods hoping to achieve a sure shot way out for the sufferings of people affected by Alzheimer besides other diseases.
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Affiliation(s)
- Kirtee Baheti
- Department of Pharmaceutical Chemistry, Government College of Pharmacy, Aurangabad-431005, Maharashtra, India
| | - Mayura Kale
- Department of Pharmaceutical Chemistry, Government College of Pharmacy, Aurangabad-431005, Maharashtra, India
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24
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Ranjan P, Athar M, Jha PC, Krishna KV. Probing the opportunities for designing anthelmintic leads by sub-structural topology-based QSAR modelling. Mol Divers 2018; 22:669-683. [PMID: 29611020 DOI: 10.1007/s11030-018-9825-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2017] [Accepted: 03/16/2018] [Indexed: 12/30/2022]
Abstract
A quantitative structure-activity (QSAR) model has been developed for enriched tubulin inhibitors, which were retrieved from sequence similarity searches and applicability domain analysis. Using partial least square (PLS) method and leave-one-out (LOO) validation approach, the model was generated with the correlation statistics of [Formula: see text] and [Formula: see text] of 0.68 and 0.69, respectively. The present study indicates that topological descriptors, viz. BIC, CH_3_C, IC, JX and Kappa_2 correlate well with biological activity. ADME and toxicity (or ADME/T) assessment showed that out of 260 molecules, 255 molecules successfully passed the ADME/T assessment test, wherein the drug-likeness attributes were exhibited. These results showed that topological indices and the colchicine binding domain directly influence the aetiology of helminthic infections. Further, we anticipate that our model can be applied for guiding and designing potential anthelmintic inhibitors.
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Affiliation(s)
- Prabodh Ranjan
- CCG@CUG, School of Chemical Sciences, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India
| | - Mohd Athar
- CCG@CUG, School of Chemical Sciences, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India
| | - Prakash Chandra Jha
- CCG@CUG, Centre for Applied Chemistry, Central University of Gujarat, Sector-30, Gandhinagar, Gujarat, 382030, India.
| | - Kari Vijaya Krishna
- Department of Chemistry, School of Advanced Sciences, VIT University, Vellore, Tamil Nadu, 632014, India
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25
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Gupta N, Pandya P, Verma S. Computational Predictions for Multi-Target Drug Design. METHODS IN PHARMACOLOGY AND TOXICOLOGY 2018. [DOI: 10.1007/7653_2018_26] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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26
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Kryshchyshyn A, Devinyak O, Kaminskyy D, Grellier P, Lesyk R. Development of Predictive QSAR Models of 4-Thiazolidinones Antitrypanosomal Activity Using Modern Machine Learning Algorithms. Mol Inform 2017; 37:e1700078. [PMID: 29134756 DOI: 10.1002/minf.201700078] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Accepted: 10/30/2017] [Indexed: 01/24/2023]
Abstract
This paper presents novel QSAR models for the prediction of antitrypanosomal activity among thiazolidines and related heterocycles. The performance of four machine learning algorithms: Random Forest regression, Stochastic gradient boosting, Multivariate adaptive regression splines and Gaussian processes regression have been studied in order to reach better levels of predictivity. The results for Random Forest and Gaussian processes regression are comparable and outperform other studied methods. The preliminary descriptor selection with Boruta method improved the outcome of machine learning methods. The two novel QSAR-models developed with Random Forest and Gaussian processes regression algorithms have good predictive ability, which was proved by the external evaluation of the test set with corresponding Q2ext =0.812 and Q2ext =0.830. The obtained models can be used further for in silico screening of virtual libraries in the same chemical domain in order to find new antitrypanosomal agents. Thorough analysis of descriptors influence in the QSAR models and interpretation of their chemical meaning allows to highlight a number of structure-activity relationships. The presence of phenyl rings with electron-withdrawing atoms or groups in para-position, increased number of aromatic rings, high branching but short chains, high HOMO energy, and the introduction of 1-substituted 2-indolyl fragment into the molecular structure have been recognized as trypanocidal activity prerequisites.
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Affiliation(s)
- Anna Kryshchyshyn
- Department of Pharmaceutical, Organic and Bioorganic Chemistry, Danylo Halytsky Lviv National Medical University, Pekarska str. 69, 79010, Lviv, Ukraine
| | - Oleg Devinyak
- Department of Pharmaceutical Disciplines, Uzhgorod National University, Narodna sq. 1, 88000, Uzhgorod, Ukraine
| | - Danylo Kaminskyy
- Department of Pharmaceutical, Organic and Bioorganic Chemistry, Danylo Halytsky Lviv National Medical University, Pekarska str. 69, 79010, Lviv, Ukraine
| | - Philippe Grellier
- National Museum of Natural History, UMR 7245 CNRS MCAM, Sorbonne Universités, CP 52, 57 Rue Cuvier, Paris, 75005, France
| | - Roman Lesyk
- Department of Pharmaceutical, Organic and Bioorganic Chemistry, Danylo Halytsky Lviv National Medical University, Pekarska str. 69, 79010, Lviv, Ukraine
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27
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Roca C, Sebastián-Pérez V, Campillo NE. In silico Tools for Target Identification and Drug Molecular Docking in Leishmania. DRUG DISCOVERY FOR LEISHMANIASIS 2017. [DOI: 10.1039/9781788010177-00130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Neglected tropical diseases represent a significant health burden in large parts of the world. Drug discovery is currently a key bottleneck in the pipeline of these diseases. In this chapter, the in silico approaches used for the processes involved in drug discovery, identification and validation of druggable Leishmania targets, and design and optimisation of new anti-leishmanial drugs are discussed. We also provide a general view of the different computational tools that can be employed in pursuit of this aim, along with the most interesting cases found in the literature.
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Affiliation(s)
- Carlos Roca
- Centro de Investigaciones Biológicas (CSIC) Ramiro de Maeztu 9 28040 Madrid Spain
| | | | - Nuria E. Campillo
- Centro de Investigaciones Biológicas (CSIC) Ramiro de Maeztu 9 28040 Madrid Spain
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28
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Speck-Planche A, Cordeiro MNDS. De novo computational design of compounds virtually displaying potent antibacterial activity and desirable in vitro ADMET profiles. Med Chem Res 2017. [DOI: 10.1007/s00044-017-1936-4] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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29
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Zhang Y, Zhu JM, Liang YB, Chen HB, Yin SM, Chen ZC. Non-invasive blood glucose detection system based on conservation of energy method. Physiol Meas 2017; 38:325-342. [PMID: 28107204 DOI: 10.1088/1361-6579/aa50cf] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The most common method used for minimizing the occurrence of diabetes complications is frequent glucose testing to adjust the insulin dose. However, using blood glucose (BG) meters presents a risk of infection. It is of great importance to develop non-invasive BG detection techniques. To realize high-accuracy, low-cost and continuous glucose monitoring, we have developed a non-invasive BG detection system using a mixed signal processor 430 (MSP430) microcontroller. This method is based on the combination of the conservation-of-energy method with a sensor integration module, which collects physiological parameters, such as the blood oxygen saturation (SPO2), blood flow velocity and heart rate. New methods to detect the basal metabolic rate (BMR) and BV are proposed, which combine the human body heat balance and characteristic signals of photoplethysmography as well dual elastic chambers theory. Four hundred clinical trials on real-time non-invasive BG monitoring under suitable experiment conditions were performed on different individuals, including diabetic patients, senior citizens and healthy adults. A multisensory information fusion model was applied to process these samples. The algorithm (we defined it as DCBPN algorithm) applied in the model combines a decision tree and back propagation neural network, which classifies the physiological and environmental parameters into three categories, and then establishes a corresponding prediction model for the three categories. The DCBPN algorithm provides an accuracy of 88.53% in predicting the BG of new samples. Thus, this system demonstrates a great potential to reliably detect BG values in a non-invasive setting.
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Affiliation(s)
- Yang Zhang
- School of Electronic Engineer and Automatic, Guilin University of Electronic Technology, GuiLin, People's Republic of China
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30
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Kleandrova VV, Luan F, Speck-Planche A, Cordeiro MNDS. QSAR-Based Studies of Nanomaterials in the Environment. PHARMACEUTICAL SCIENCES 2017. [DOI: 10.4018/978-1-5225-1762-7.ch051] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Nanotechnology is a newly emerging field, posing substantial impacts on society, economy, and the environment. In recent years, the development of nanotechnology has led to the design and large-scale production of many new materials and devices with a vast range of applications. However, along with the benefits, the use of nanomaterials raises many questions and generates concerns due to the possible health-risks and environmental impacts. This chapter provides an overview of the Quantitative Structure-Activity Relationships (QSAR) studies performed so far towards predicting nanoparticles' environmental toxicity. Recent progresses on the application of these modeling studies are additionally pointed out. Special emphasis is given to the setup of a QSAR perturbation-based model for the assessment of ecotoxic effects of nanoparticles in diverse conditions. Finally, ongoing challenges that may lead to new and exciting directions for QSAR modeling are discussed.
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Affiliation(s)
| | - Feng Luan
- Yantai University, China & University of Porto, Portugal
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31
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Speck-Planche A, Kleandrova VV, Ruso JM, Cordeiro MNDS. First Multitarget Chemo-Bioinformatic Model To Enable the Discovery of Antibacterial Peptides against Multiple Gram-Positive Pathogens. J Chem Inf Model 2016; 56:588-98. [PMID: 26960000 DOI: 10.1021/acs.jcim.5b00630] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Antimicrobial peptides (AMPs) have emerged as promising therapeutic alternatives to fight against the diverse infections caused by different pathogenic microorganisms. In this context, theoretical approaches in bioinformatics have paved the way toward the creation of several in silico models capable of predicting antimicrobial activities of peptides. All current models have several significant handicaps, which prevent the efficient search for highly active AMPs. Here, we introduce the first multitarget (mt) chemo-bioinformatic model devoted to performing alignment-free prediction of antibacterial activity of peptides against multiple Gram-positive bacterial strains. The model was constructed from a data set containing 2488 cases of AMPs sequences assayed against at least 1 out of 50 Gram-positive bacterial strains. This mt-chemo-bioinformatic model displayed percentages of correct classification higher than 90.00% in both training and prediction (test) sets. For the first time, two computational approaches derived from basic concepts in genetics and molecular biology were applied, allowing the calculations of the relative contributions of any amino acid (in a defined position) to the antibacterial activity of an AMP and depending on the bacterial strain used in the biological assay. The present mt-chemo-bioinformatic model constitutes a powerful tool to enable the discovery of potent and versatile AMPs.
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Affiliation(s)
- Alejandro Speck-Planche
- Department of Applied Physics, University of Santiago de Compostela (USC) , 15782 Santiago de Compostela, Spain.,REQUIMTE/Department of Chemistry and Biochemistry, University of Porto , 4169-007 Porto, Portugal
| | - Valeria V Kleandrova
- Faculty of Technology and Production Management, Moscow State University of Food Production , Volokolamskoe shosse 11, 125080 Moscow, Russia
| | - Juan M Ruso
- Department of Applied Physics, University of Santiago de Compostela (USC) , 15782 Santiago de Compostela, Spain
| | - M N D S Cordeiro
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto , 4169-007 Porto, Portugal
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Castillo-Garit JA, del Toro-Cortés O, Vega MC, Rolón M, Rojas de Arias A, Casañola-Martin GM, Escario JA, Gómez-Barrio A, Marrero-Ponce Y, Torrens F, Abad C. Bond-based bilinear indices for computational discovery of novel trypanosomicidal drug-like compounds through virtual screening. Eur J Med Chem 2015; 96:238-44. [PMID: 25884114 DOI: 10.1016/j.ejmech.2015.03.063] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Revised: 02/27/2015] [Accepted: 03/27/2015] [Indexed: 11/25/2022]
Abstract
Two-dimensional bond-based bilinear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop the theoretical models. Two discriminant models, computed using bond-based bilinear indices, are developed and both show accuracies higher than 86% for training and test sets. The stochastic model correctly indentifies nine out of eleven compounds of a set of organic chemicals obtained from our synthetic collaborators. The in vitro antitrypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a good agreement between theoretical predictions and experimental results. Three compounds showed IC50 values for epimastigote elimination (AE) lower than 50 μM, while for the benznidazole the IC50 = 54.7 μM which was used as reference compound. The value of IC50 for cytotoxicity of these compounds is at least 5 times greater than their value of IC50 for AE. Finally, we can say that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new antitrypanosomal compounds.
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Affiliation(s)
- Juan Alberto Castillo-Garit
- Centro de Estudio de Química Aplicada, Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba; Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba; Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100, Burjassot, Spain; Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P.O. Box 22085, E-46071, València, Spain.
| | - Oremia del Toro-Cortés
- Centro de Estudio de Química Aplicada, Facultad de Química-Farmacia, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba
| | - Maria C Vega
- Centro para el Desarrollo de la Investigacion Cientifica (CEDIC) and Fundación Moisés Bertoni/Laboratorios Díaz Gill, Pai Perez 265 casi Mariscal Estigarribia, Asuncion, Paraguay
| | - Miriam Rolón
- Centro para el Desarrollo de la Investigacion Cientifica (CEDIC) and Fundación Moisés Bertoni/Laboratorios Díaz Gill, Pai Perez 265 casi Mariscal Estigarribia, Asuncion, Paraguay
| | - Antonieta Rojas de Arias
- Centro para el Desarrollo de la Investigacion Cientifica (CEDIC) and Fundación Moisés Bertoni/Laboratorios Díaz Gill, Pai Perez 265 casi Mariscal Estigarribia, Asuncion, Paraguay
| | - Gerardo M Casañola-Martin
- Unit of Computer-Aided Molecular "Biosilico" Discovery and Bioinformatic Research (CAMD-BIR Unit), Faculty of Chemistry-Pharmacy, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, 54830, Villa Clara, Cuba; Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100, Burjassot, Spain; Centro de Información y Gestión Tecnológica, Ministerio de Ciencia Tecnología y Medio Ambiente (CITMA), 65100, Ciego de Ávila, Cuba
| | - José A Escario
- Departamento de Parasitología, Facultad de Farmacia, UCM, Pza. Ramón y Cajal s/n, 28040, Madrid, Spain
| | - Alicia Gómez-Barrio
- Departamento de Parasitología, Facultad de Farmacia, UCM, Pza. Ramón y Cajal s/n, 28040, Madrid, Spain
| | - Yovani Marrero-Ponce
- Enviromental and Computational Chemistry Group, Facultad de Química Farmacéutica, Universidad de Cartagena,Cartagena de Indias, Bolivar, Colombia
| | - Francisco Torrens
- Institut Universitari de Ciència Molecular, Universitat de València, Edifici d'Instituts de Paterna, P.O. Box 22085, E-46071, València, Spain
| | - Concepción Abad
- Departament de Bioquímica i Biologia Molecular, Universitat de València, E-46100, Burjassot, Spain
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Predicting antiprotozoal activity of benzyl phenyl ether diamine derivatives through QSAR multi-target and molecular topology. Mol Divers 2015; 19:357-66. [DOI: 10.1007/s11030-015-9575-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2014] [Accepted: 02/22/2015] [Indexed: 01/12/2023]
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Du XH, Zhuang WC, Shi XQ, Feng CJ. Research on Thermodynamic Properties of Polybrominated Diphenylamine by Neural Network. CHINESE J CHEM PHYS 2015. [DOI: 10.1063/1674-0068/28/cjcp1406109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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Speck-Planche A, Cordeiro MNDS. Multitasking models for quantitative structure–biological effect relationships: current status and future perspectives to speed up drug discovery. Expert Opin Drug Discov 2015; 10:245-56. [DOI: 10.1517/17460441.2015.1006195] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
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Speck-Planche A, Cordeiro MNDS. A general ANN-based multitasking model for the discovery of potent and safer antibacterial agents. Methods Mol Biol 2015; 1260:45-64. [PMID: 25502375 DOI: 10.1007/978-1-4939-2239-0_4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Bacteria have been one of the world's most dangerous and deadliest pathogens for mankind, nowadays giving rise to significant public health concerns. Given the prevalence of these microbial pathogens and their increasing resistance to existing antibiotics, there is a pressing need for new antibacterial drugs. However, development of a successful drug is a complex, costly, and time-consuming process. Quantitative Structure-Activity Relationships (QSAR)-based approaches are valuable tools for shortening the time of lead compound identification but also for focusing and limiting time-costly synthetic activities and in vitro/vivo evaluations. QSAR-based approaches, supported by powerful statistical techniques such as artificial neural networks (ANNs), have evolved to the point of integrating dissimilar types of chemical and biological data. This chapter reports an overview of the current research and potential applications of QSAR modeling tools toward the rational design of more efficient antibacterial agents. Particular emphasis is given to the setup of multitasking models along with ANNs aimed at jointly predicting different antibacterial activities and safety profiles of drugs/chemicals under diverse experimental conditions.
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Affiliation(s)
- A Speck-Planche
- Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007, Porto, Portugal
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37
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Chemoinformatics for medicinal chemistry: in silico model to enable the discovery of potent and safer anti-cocci agents. Future Med Chem 2014; 6:2013-28. [DOI: 10.4155/fmc.14.136] [Citation(s) in RCA: 34] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background: Gram-positive cocci are increasingly antibiotic-resistant bacteria responsible for causing serious diseases. Chemoinformatics can help to rationalize the discovery of more potent and safer antibacterial drugs. We have developed a chemoinformatic model for simultaneous prediction of anti-cocci activities, and profiles involving absorption, distribution, metabolism, elimination and toxicity (ADMET). Results: A dataset containing 48,874 cases from many different chemicals assayed under dissimilar experimental conditions was created. The best model displayed accuracies around 93% in both training and prediction (test) sets. Quantitative contributions of several fragments to the biological effects were calculated and analyzed. Multiple biological effects of the investigational drug JNJ-Q2 were correctly predicted. Conclusion: Our chemoinformatic model can be used as powerful tool for virtual screening of promising anti-cocci agents.
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Ziarek JJ, Liu Y, Smith E, Zhang G, Peterson FC, Chen J, Yu Y, Chen Y, Volkman BF, Li R. Fragment-based optimization of small molecule CXCL12 inhibitors for antagonizing the CXCL12/CXCR4 interaction. Curr Top Med Chem 2013; 12:2727-40. [PMID: 23368099 DOI: 10.2174/1568026611212240003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2012] [Revised: 10/08/2012] [Accepted: 11/03/2012] [Indexed: 12/21/2022]
Abstract
The chemokine CXCL12 and its G protein-coupled receptor (GPCR) CXCR4 are high-priority clinical targets because of their involvement in metastatic cancers (also implicated in autoimmune disease and cardiovascular disease). Because chemokines interact with two distinct sites to bind and activate their receptors, both the GPCRs and chemokines are potential targets for small molecule inhibition. A number of chemokines have been validated as targets for drug development, but virtually all drug discovery efforts focus on the GPCRs. However, all CXCR4 receptor antagonists with the exception of MSX-122 have failed in clinical trials due to unmanageable toxicities, emphasizing the need for alternative strategies to interfere with CXCL12/CXCR4-guided metastatic homing. Although targeting the relatively featureless surface of CXCL12 was presumed to be challenging, focusing efforts at the sulfotyrosine (sY) binding pockets proved successful for procuring initial hits. Using a hybrid structure-based in silico/NMR screening strategy, we recently identified a ligand that occludes the receptor recognition site. From this initial hit, we designed a small fragment library containing only nine tetrazole derivatives using a fragment-based and bioisostere approach to target the sY binding sites of CXCL12. Compound binding modes and affinities were studied by 2D NMR spectroscopy, X-ray crystallography, molecular docking and cell-based functional assays. Our results demonstrate that the sY binding sites are conducive to the development of high affinity inhibitors with better ligand efficiency (LE) than typical protein-protein interaction inhibitors (LE ≤ 0.24). Our novel tetrazole-based fragment 18 was identified to bind the sY21 site with a K(d) of 24 μM (LE = 0.30). Optimization of 18 yielded compound 25 which specifically inhibits CXCL12-induced migration with an improvement in potency over the initial hit 9. The fragment from this library that exhibited the highest affinity and ligand efficiency (11: K(d) = 13 μM, LE = 0.33) may serve as a starting point for development of inhibitors targeting the sY12 site.
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Affiliation(s)
- Joshua J Ziarek
- Department of Biochemistry, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA
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Chemoinformatics for rational discovery of safe antibacterial drugs: simultaneous predictions of biological activity against streptococci and toxicological profiles in laboratory animals. Bioorg Med Chem 2013; 21:2727-32. [PMID: 23582445 DOI: 10.1016/j.bmc.2013.03.015] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2012] [Revised: 03/03/2013] [Accepted: 03/04/2013] [Indexed: 11/21/2022]
Abstract
Streptococci are a group of Gram-positive bacteria which are responsible for causing many diverse diseases in humans and other animals worldwide. The high prevalence of resistance of these bacteria to current antibacterial drugs is an alarming problem for the scientific community. The battle against streptococci by using antimicrobial chemotherapies will depend on the design of new chemicals with high inhibitory activity, having also as low toxicity as possible. Multi-target approaches based on quantitative-structure activity relationships (mt-QSAR) have played a very important role, providing a better knowledge about the molecular patterns related with the appearance of different pharmacological profiles including antimicrobial activity. Until now, almost all mt-QSAR models have considered the study of biological activity or toxicity separately. In the present study, we develop by the first time, a unified multitasking (mtk) QSAR model for the simultaneous prediction of anti-streptococci activity and toxic effects against biological models like Mus musculus and Rattus norvegicus. The mtk-QSAR model was created by using artificial neural networks (ANN) analysis for the classification of compounds as positive (high biological activity and/or low toxicity) or negative (otherwise) under diverse sets of experimental conditions. Our mtk-QSAR model, correctly classified more than 97% of the cases in the whole database (more than 11,500 cases), serving as a promising tool for the virtual screening of potent and safe anti-streptococci drugs.
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Cao DS, Xu QS, Hu QN, Liang YZ. ChemoPy: freely available python package for computational biology and chemoinformatics. ACTA ACUST UNITED AC 2013; 29:1092-4. [PMID: 23493324 DOI: 10.1093/bioinformatics/btt105] [Citation(s) in RCA: 123] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
MOTIVATION Molecular representation for small molecules has been routinely used in QSAR/SAR, virtual screening, database search, ranking, drug ADME/T prediction and other drug discovery processes. To facilitate extensive studies of drug molecules, we developed a freely available, open-source python package called chemoinformatics in python (ChemoPy) for calculating the commonly used structural and physicochemical features. It computes 16 drug feature groups composed of 19 descriptors that include 1135 descriptor values. In addition, it provides seven types of molecular fingerprint systems for drug molecules, including topological fingerprints, electro-topological state (E-state) fingerprints, MACCS keys, FP4 keys, atom pairs fingerprints, topological torsion fingerprints and Morgan/circular fingerprints. By applying a semi-empirical quantum chemistry program MOPAC, ChemoPy can also compute a large number of 3D molecular descriptors conveniently. AVAILABILITY The python package, ChemoPy, is freely available via http://code.google.com/p/pychem/downloads/list, and it runs on Linux and MS-Windows. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dong-Sheng Cao
- Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha, P. R. China
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41
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Speck-Planche A, Kleandrova VV, Cordeiro MND. New insights toward the discovery of antibacterial agents: Multi-tasking QSBER model for the simultaneous prediction of anti-tuberculosis activity and toxicological profiles of drugs. Eur J Pharm Sci 2013; 48:812-8. [DOI: 10.1016/j.ejps.2013.01.011] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2012] [Revised: 01/05/2013] [Accepted: 01/23/2013] [Indexed: 01/11/2023]
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Abstract
Frequent failure of drug candidates during development stages remains the major deterrent for an early introduction of new drug molecules. The drug toxicity is the major cause of expensive late-stage development failures. An early identification/optimization of the most favorable molecule will naturally save considerable cost, time, human efforts and minimize animal sacrifice. (Quantitative) Structure Activity Relationships [(Q)SARs] represent statistically derived predictive models correlating biological activity (including desirable therapeutic effect and undesirable side effects) of chemicals (drugs/toxicants/environmental pollutants) with molecular descriptors and/or properties. (Q)SAR models which categorize the available data into two or more groups/classes are known as classification models. Numerous techniques of diverse nature are being presently employed for development of classification models. Though there is an increasing use of classification models for prediction of either biological activity or toxicity, the future trend will naturally be towards the development of classification models capable of simultaneous prediction of biological activity, toxicity, and pharmacokinetic parameters so as to accelerate development of bioavailable safe drug molecules.
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Mor S, Pahal P, Narasimhan B. Synthesis, characterization, biological evaluation and QSAR studies of 11-p-substituted phenyl-12-phenyl-11a,12-dihydro-11H-indeno[2,1-c][1,5]benzothiazepines as potential antimicrobial agents. Eur J Med Chem 2012; 57:196-210. [DOI: 10.1016/j.ejmech.2012.09.003] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2012] [Revised: 08/31/2012] [Accepted: 09/03/2012] [Indexed: 10/27/2022]
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ANN multiplexing model of drugs effect on macrophages; theoretical and flow cytometry study on the cytotoxicity of the anti-microbial drug G1 in spleen. Bioorg Med Chem 2012; 20:6181-94. [DOI: 10.1016/j.bmc.2012.07.020] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2012] [Revised: 07/11/2012] [Accepted: 07/13/2012] [Indexed: 11/19/2022]
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Anusuya S, Natarajan J. Multi-targeted therapy for leprosy: insilico strategy to overcome multi drug resistance and to improve therapeutic efficacy. INFECTION GENETICS AND EVOLUTION 2012; 12:1899-910. [PMID: 22981928 DOI: 10.1016/j.meegid.2012.08.013] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2012] [Revised: 08/01/2012] [Accepted: 08/17/2012] [Indexed: 02/02/2023]
Abstract
Leprosy remains a major public health problem, since single and multi-drug resistance has been reported worldwide over the last two decades. In the present study, we report the novel multi-targeted therapy for leprosy to overcome multi drug resistance and to improve therapeutic efficacy. If multiple enzymes of an essential metabolic pathway of a bacterium were targeted, then the therapy would become more effective and can prevent the occurrence of drug resistance. The MurC, MurD, MurE and MurF enzymes of peptidoglycan biosynthetic pathway were selected for multi targeted therapy. The conserved or class specific active site residues important for function or stability were predicted using evolutionary trace analysis and site directed mutagenesis studies. Ten such residues which were present in at least any three of the four Mur enzymes (MurC, MurD, MurE and MurF) were identified. Among the ten residues G125, K126, T127 and G293 (numbered based on their position in MurC) were found to be conserved in all the four Mur enzymes of the entire bacterial kingdom. In addition K143, T144, T166, G168, H234 and Y329 (numbered based on their position in MurE) were significant in binding substrates and/co-factors needed for the functional events in any three of the Mur enzymes. These are the probable residues for designing newer anti-leprosy drugs in an attempt to reduce drug resistance.
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Affiliation(s)
- Shanmugam Anusuya
- Department of Bioinformatics, VMKV Engineering College, Vinayaka Missions University, Salem 636 308, India.
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46
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Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. Rational drug design for anti-cancer chemotherapy: Multi-target QSAR models for the in silico discovery of anti-colorectal cancer agents. Bioorg Med Chem 2012; 20:4848-55. [DOI: 10.1016/j.bmc.2012.05.071] [Citation(s) in RCA: 60] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2012] [Revised: 05/21/2012] [Accepted: 05/25/2012] [Indexed: 12/23/2022]
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Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MND. Chemoinformatics in anti-cancer chemotherapy: Multi-target QSAR model for the in silico discovery of anti-breast cancer agents. Eur J Pharm Sci 2012; 47:273-9. [DOI: 10.1016/j.ejps.2012.04.012] [Citation(s) in RCA: 56] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2012] [Revised: 03/22/2012] [Accepted: 04/09/2012] [Indexed: 12/25/2022]
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48
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Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. A ligand-based approach for the in silico discovery of multi-target inhibitors for proteins associated with HIV infection. MOLECULAR BIOSYSTEMS 2012; 8:2188-96. [PMID: 22688327 DOI: 10.1039/c2mb25093d] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Acquired immunodeficiency syndrome (AIDS) is a dangerous disease, which damages the immune system cells to the point that the immune system can no longer fight against other infections that it would usually be able to prevent. The causal agent is the human immunodeficiency virus (HIV), and for this reason, the search for more effective chemotherapies against HIV is a challenge for the scientific community. Chemoinformatics and Quantitative Structure-Activity Relationship (QSAR) studies have played an essential role in the design of potent inhibitors for proteins associated with the HIV infection. However, all previous studies took into consideration the discovery of future drug candidates using homogeneous series of compounds against only one protein. This fact limits the use of more efficient anti-HIV chemotherapies. In this work, we develop the first ligand-based approach for the in silico design of multi-target (mt) inhibitors for seven key proteins associated with the HIV infection. Two mt-QSAR models were constructed from a large and heterogeneous database of compounds. The first model was based on linear discriminant analysis (mt-QSAR-LDA) employing fragment-based descriptors. The second model was obtained using artificial neural networks (mt-QSAR-ANN) with global 2D descriptors. Both models correctly classified more than 90% of active and inactive compounds in training and prediction sets. Some fragments were extracted and their contributions to anti-HIV activity through inhibition of the different proteins were calculated using the mt-QSAR-LDA model. New molecules designed from fragments with positive contributions were suggested and correctly predicted by the two models as possible potent and versatile anti-HIV agents.
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Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
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Speck-Planche A, Kleandrova VV, Luan F, Cordeiro MNDS. Predicting multiple ecotoxicological profiles in agrochemical fungicides: a multi-species chemoinformatic approach. ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY 2012; 80:308-313. [PMID: 22521812 DOI: 10.1016/j.ecoenv.2012.03.018] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2012] [Revised: 03/22/2012] [Accepted: 03/23/2012] [Indexed: 05/31/2023]
Abstract
Agriculture is needed to deal with crop losses caused by biotic stresses like pests. The use of pesticides has played a vital role, contributing to improve crop production and harvest productivity, providing a better crop quality and supply, and consequently contributing with the improvement of the human health. An important group of these pesticides is fungicides. However, the use of these agrochemical fungicides is an important source of contamination, damaging the ecosystems. Several studies have been realized for the assessment of the toxicity in agrochemical fungicides, but the principal limitation is the use of structurally related compounds against usually one indicator species. In order to overcome this problem, we explore the quantitative structure-toxicity relationships (QSTR) in agrochemical fungicides. Here, we developed the first multi-species (ms) chemoinformatic approach for the prediction multiple ecotoxicological profiles of fungicides against 20 indicators species and their classifications in toxic or nontoxic. The ms-QSTR discriminant model was based on substructural descriptors and a heterogeneous database of compounds. The percentages of correct classification were higher than 90% for both, training and prediction series. Also, substructural alerts responsible for the toxicity/no toxicity in fungicides respect all ecotoxicological profiles, were extracted and analyzed.
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Affiliation(s)
- Alejandro Speck-Planche
- REQUIMTE/Department of Chemistry and Biochemistry, University of Porto, 4169-007 Porto, Portugal.
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2D MI-DRAGON: A new predictor for protein–ligands interactions and theoretic-experimental studies of US FDA drug-target network, oxoisoaporphine inhibitors for MAO-A and human parasite proteins. Eur J Med Chem 2011; 46:5838-51. [DOI: 10.1016/j.ejmech.2011.09.045] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2011] [Revised: 09/22/2011] [Accepted: 09/26/2011] [Indexed: 11/20/2022]
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