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Xiao H, Li J, Yang D, Du J, Li J, Lin S, Zhou H, Sun P, Xu J. Multidimensional Criteria for Virtual Screening of PqsR Inhibitors Based on Pharmacophore, Docking, and Molecular Dynamics. Int J Mol Sci 2024; 25:1869. [PMID: 38339148 PMCID: PMC10856439 DOI: 10.3390/ijms25031869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/27/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
Pseudomonas aeruginosa is a clinically challenging pathogen due to its high resistance to antibiotics. Quorum sensing inhibitors (QSIs) have been proposed as a promising strategy to overcome this resistance by interfering with the bacterial communication system. Among the potential targets of QSIs, PqsR is a key regulator of quorum sensing in Pseudomonas aeruginosa. However, the current research on PqsR inhibitors is limited by the lack of diversity in the chemical structures and the screening methods. Therefore, this study aims to develop a multidimensional screening model for PqsR inhibitors based on both ligand- and receptor-based approaches. First, a pharmacophore model was constructed from a training set of PqsR inhibitors to identify the essential features and spatial arrangement for the activity. Then, molecular docking and dynamics simulations were performed to explore the core interactions between PqsR inhibitors and their receptor. The results indicate that an effective PqsR inhibitor should possess two aromatic rings, one hydrogen bond acceptor, and two hydrophobic groups and should form strong interactions with the following four amino acid residues: TYR_258, ILE_236, LEU_208, and GLN_194. Moreover, the docking score and the binding free energy should be lower than -8 kcal/mol and -40 kcal/mol, respectively. Finally, the validity of the multidimensional screening model was confirmed by a test set of PqsR inhibitors, which showed a higher accuracy than the existing screening methods based on single characteristics. This multidimensional screening model would be a useful tool for the discovery and optimization of PqsR inhibitors in the future.
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Affiliation(s)
- Haichuan Xiao
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
| | - Jiahao Li
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
| | - Dongdong Yang
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
| | - Jiarui Du
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
| | - Jie Li
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
| | - Shuqi Lin
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
| | - Haibo Zhou
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
| | - Pinghua Sun
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
- Key Laboratory of Xinjiang Phytomedicine Resource and Utilization, Ministry of Education, School of Pharmacy, Shihezi University, Shihezi 832003, China
| | - Jun Xu
- College of Pharmacy, Jinan University, Guangzhou 510632, China; (H.X.); (J.L.); (D.Y.); (J.D.); (J.L.); (S.L.); (H.Z.)
- State Key Laboratory of Bioactive Molecules and Druggability Assessment, Jinan University, Guangzhou 510632, China
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Kleandrova VV, Cordeiro MNDS, Speck-Planche A. Optimizing drug discovery using multitasking models for quantitative structure-biological effect relationships: an update of the literature. Expert Opin Drug Discov 2023; 18:1231-1243. [PMID: 37639708 DOI: 10.1080/17460441.2023.2251385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
INTRODUCTION Drug discovery has provided modern societies with the means to fight against many diseases. In this sense, computational methods have been at the forefront, playing an important role in rationalizing the search for novel drugs. Yet, tackling phenomena such as the multi-genic nature of diseases and drug resistance are limitations of the current computational methods. Multi-tasking models for quantitative structure-biological effect relationships (mtk-QSBER) have emerged to overcome such limitations. AREAS COVERED The present review describes an update on the fundamentals and applications of the mtk-QSBER models as tools to accelerate multiple stages/substages of the drug discovery process. EXPERT OPINION Computational approaches are extremely important for the rationalization of the search for novel and efficacious therapeutic agents. However, they need to focus more on the multi-target drug discovery paradigm. In this sense, mtk-QSBER models are particularly suited for multi-target drug discovery, offering encouraging opportunities across multiple therapeutic areas and scientific disciplines associated with drug discovery.
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Affiliation(s)
- Valeria V Kleandrova
- Laboratory of Fundamental and Applied Research of Quality and Technology of Food Production, Russian Biotechnological University, Moscow, Russian Federation
| | - M Natália D S Cordeiro
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
| | - Alejandro Speck-Planche
- LAQV@REQUIMTE/Department of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, Porto, Portugal
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system. Mol Divers 2022; 27:959-985. [PMID: 35819579 DOI: 10.1007/s11030-022-10489-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/21/2022] [Indexed: 12/11/2022]
Abstract
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
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Diéguez-Santana K, Casañola-Martin GM, Torres R, Rasulev B, Green JR, González-Díaz H. Machine Learning Study of Metabolic Networks vs ChEMBL Data of Antibacterial Compounds. Mol Pharm 2022; 19:2151-2163. [PMID: 35671399 PMCID: PMC9986951 DOI: 10.1021/acs.molpharmaceut.2c00029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Antibacterial drugs (AD) change the metabolic status of bacteria, contributing to bacterial death. However, antibiotic resistance and the emergence of multidrug-resistant bacteria increase interest in understanding metabolic network (MN) mutations and the interaction of AD vs MN. In this study, we employed the IFPTML = Information Fusion (IF) + Perturbation Theory (PT) + Machine Learning (ML) algorithm on a huge dataset from the ChEMBL database, which contains >155,000 AD assays vs >40 MNs of multiple bacteria species. We built a linear discriminant analysis (LDA) and 17 ML models centered on the linear index and based on atoms to predict antibacterial compounds. The IFPTML-LDA model presented the following results for the training subset: specificity (Sp) = 76% out of 70,000 cases, sensitivity (Sn) = 70%, and Accuracy (Acc) = 73%. The same model also presented the following results for the validation subsets: Sp = 76%, Sn = 70%, and Acc = 73.1%. Among the IFPTML nonlinear models, the k nearest neighbors (KNN) showed the best results with Sn = 99.2%, Sp = 95.5%, Acc = 97.4%, and Area Under Receiver Operating Characteristic (AUROC) = 0.998 in training sets. In the validation series, the Random Forest had the best results: Sn = 93.96% and Sp = 87.02% (AUROC = 0.945). The IFPTML linear and nonlinear models regarding the ADs vs MNs have good statistical parameters, and they could contribute toward finding new metabolic mutations in antibiotic resistance and reducing time/costs in antibacterial drug research.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Gerardo M Casañola-Martin
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States.,Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Roldan Torres
- Universidad Regional Amazónica IKIAM, Tena, Napo 150150, Ecuador
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58102, United States
| | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S5B6 Ottawa, Ontario, Canada
| | - Humbert González-Díaz
- Department of Organic and Inorganic Chemistry, University of Basque Country UPV/EHU, 48940 Leioa, Spain.,BIOFISIKA, Basque Center for Biophysics CSIC-UPVEH, 48940 Leioa, Spain.,IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Biscay, Spain
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Khan A, Poojary SS, Bhave KK, Nandan SR, Iyer KR, Coutinho EC. Prediction of QcrB Inhibition as a Measure of Antitubercular Activity with Machine Learning Protocols. ACS OMEGA 2022; 7:18094-18102. [PMID: 35664614 PMCID: PMC9161412 DOI: 10.1021/acsomega.2c01613] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/06/2022] [Indexed: 05/07/2023]
Abstract
It has always been a challenge to develop interventional therapies for Mycobacterium tuberculosis. Over the years, several attempts at developing such therapies have hit a dead-end owing to rapid mutation rates of the tubercular bacilli and their ability to lay dormant for years. Recently, cytochrome bcc complex (QcrB) has shown some promise as a novel target against the tubercular bacilli, with Q203 being the first molecule acting on this target. In this paper, we report the deployment of several ML-based approaches to design molecules against QcrB. Machine learning (ML) models were developed based on a data set of 350 molecules using three different sets of molecular features, i.e., MACCS keys, ECFP6 fingerprints, and Mordred descriptors. Each feature set was trained on eight ML classifier algorithms and optimized to classify molecules accurately. The support vector machine-based classifier using the ECFP6 feature set was found to be the best classifier in this study. Further, screening of the known imidazopyridine amide inhibitors demonstrated that the model correctly classified the most potent molecules as actives, hence validating the model for future applications.
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Affiliation(s)
- Afreen
A. Khan
- Department
of Pharmaceutical Chemistry, Vasvik Research Centre, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India
| | - Sannidhi S. Poojary
- Department
of Pharmaceutical Chemistry, Vasvik Research Centre, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India
| | - Ketki K. Bhave
- Department
of Pharmaceutical Chemistry, Vasvik Research Centre, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India
| | - Santosh R. Nandan
- Ambernath
Organics Pvt. Ltd., 222,
The Summit Business Bay, Andheri (E), Mumbai 400 093, India
| | - Krishna R. Iyer
- Department
of Pharmaceutical Chemistry, Vasvik Research Centre, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India
| | - Evans C. Coutinho
- Department
of Pharmaceutical Chemistry, Vasvik Research Centre, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India
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Computational Drug Repurposing for Antituberculosis Therapy: Discovery of Multi-Strain Inhibitors. Antibiotics (Basel) 2021; 10:antibiotics10081005. [PMID: 34439055 PMCID: PMC8388932 DOI: 10.3390/antibiotics10081005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 08/15/2021] [Accepted: 08/17/2021] [Indexed: 12/13/2022] Open
Abstract
Tuberculosis remains the most afflicting infectious disease known by humankind, with one quarter of the population estimated to have it in the latent state. Discovering antituberculosis drugs is a challenging, complex, expensive, and time-consuming task. To overcome the substantial costs and accelerate drug discovery and development, drug repurposing has emerged as an attractive alternative to find new applications for “old” drugs and where computational approaches play an essential role by filtering the chemical space. This work reports the first multi-condition model based on quantitative structure–activity relationships and an ensemble of neural networks (mtc-QSAR-EL) for the virtual screening of potential antituberculosis agents able to act as multi-strain inhibitors. The mtc-QSAR-EL model exhibited an accuracy higher than 85%. A physicochemical and fragment-based structural interpretation of this model was provided, and a large dataset of agency-regulated chemicals was virtually screened, with the mtc-QSAR-EL model identifying already proven antituberculosis drugs while proposing chemicals with great potential to be experimentally repurposed as antituberculosis (multi-strain inhibitors) agents. Some of the most promising molecules identified by the mtc-QSAR-EL model as antituberculosis agents were also confirmed by another computational approach, supporting the capabilities of the mtc-QSAR-EL model as an efficient tool for computational drug repurposing.
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Diéguez-Santana K, Casañola-Martin GM, Green JR, Rasulev B, González-Díaz H. Predicting Metabolic Reaction Networks with Perturbation-Theory Machine Learning (PTML) Models. Curr Top Med Chem 2021; 21:819-827. [PMID: 33797370 DOI: 10.2174/1568026621666210331161144] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Revised: 12/30/2020] [Accepted: 01/07/2021] [Indexed: 11/22/2022]
Abstract
BACKGROUND Checking the connectivity (structure) of complex Metabolic Reaction Networks (MRNs) models proposed for new microorganisms with promising properties is an important goal for chemical biology. OBJECTIVE In principle, we can perform a hand-on checking (Manual Curation). However, this is a challenging task due to the high number of combinations of pairs of nodes (possible metabolic reactions). RESULTS The CPTML linear model obtained using the LDA algorithm is able to discriminate nodes (metabolites) with the correct assignation of reactions from incorrect nodes with values of accuracy, specificity, and sensitivity in the range of 85-100% in both training and external validation data series. METHODS In this work, we used Combinatorial Perturbation Theory and Machine Learning techniques to seek a CPTML model for MRNs >40 organisms compiled by Barabasis' group. First, we quantified the local structure of a very large set of nodes in each MRN using a new class of node index called Markov linear indices fk. Next, we calculated CPT operators for 150000 combinations of query and reference nodes of MRNs. Last, we used these CPT operators as inputs of different ML algorithms. CONCLUSION Meanwhile, PTML models based on Bayesian network, J48-Decision Tree and Random Forest algorithms were identified as the three best non-linear models with accuracy greater than 97.5%. The present work opens the door to the study of MRNs of multiple organisms using PTML models.
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Affiliation(s)
- Karel Diéguez-Santana
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, and Basque Center for Biophysics CSIC-UPV/EHU, Leioa 48940, Great Bilbao, Biscay, Basque Country, Spain
| | | | - James R Green
- Department of Systems and Computer Engineering, Carleton University, K1S 5B6, Ottawa, ON, Canada
| | - Bakhtiyor Rasulev
- Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, ND 58102, United States
| | - Humberto González-Díaz
- Department of Organic and Inorganic Chemistry, University of the Basque Country UPV/EHU, and Basque Center for Biophysics CSIC-UPV/EHU, Leioa 48940, Great Bilbao, Biscay, Basque Country, Spain
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Vatansever S, Schlessinger A, Wacker D, Kaniskan HÜ, Jin J, Zhou M, Zhang B. Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions. Med Res Rev 2021; 41:1427-1473. [PMID: 33295676 PMCID: PMC8043990 DOI: 10.1002/med.21764] [Citation(s) in RCA: 95] [Impact Index Per Article: 31.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Revised: 10/30/2020] [Accepted: 11/20/2020] [Indexed: 01/11/2023]
Abstract
Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.
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Affiliation(s)
- Sezen Vatansever
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Avner Schlessinger
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Daniel Wacker
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of NeuroscienceIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - H. Ümit Kaniskan
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Jian Jin
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Therapeutics DiscoveryIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Ming‐Ming Zhou
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Oncological Sciences, Tisch Cancer InstituteIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Bin Zhang
- Department of Genetics and Genomic SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Mount Sinai Center for Transformative Disease ModelingIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Icahn Institute for Data Science and Genomic TechnologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
- Department of Pharmacological SciencesIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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Kleandrova VV, Speck-Planche A. PTML Modeling for Alzheimer’s Disease: Design and Prediction of Virtual Multi-Target Inhibitors of GSK3B, HDAC1, and HDAC6. Curr Top Med Chem 2020; 20:1661-1676. [DOI: 10.2174/1568026620666200607190951] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/12/2019] [Accepted: 01/05/2020] [Indexed: 01/23/2023]
Abstract
Background:
Alzheimer’s disease is characterized by a progressive pattern of cognitive and
functional impairment, which ultimately leads to death. Computational approaches have played an important
role in the context of drug discovery for anti-Alzheimer's therapies. However, most of the computational
models reported to date have been focused on only one protein associated with Alzheimer's,
while relying on small datasets of structurally related molecules.
Objective:
We introduce the first model combining perturbation theory and machine learning based on
artificial neural networks (PTML-ANN) for simultaneous prediction and design of inhibitors of three
Alzheimer’s disease-related proteins, namely glycogen synthase kinase 3 beta (GSK3B), histone deacetylase
1 (HDAC1), and histone deacetylase 6 (HDAC6).
Methods:
The PTML-ANN model was obtained from a dataset retrieved from ChEMBL, and it relied on
a classification approach to predict chemicals as active or inactive.
Results:
The PTML-ANN model displayed sensitivity and specificity higher than 85% in both training
and test sets. The physicochemical and structural interpretation of the molecular descriptors in the model
permitted the direct extraction of fragments suggested to favorably contribute to enhancing the multitarget
inhibitory activity. Based on this information, we assembled ten molecules from several fragments
with positive contributions. Seven of these molecules were predicted as triple target inhibitors while the
remaining three were predicted as dual-target inhibitors. The estimated physicochemical properties of
the designed molecules complied with Lipinski’s rule of five and its variants.
Conclusion:
This work opens new horizons toward the design of multi-target inhibitors for anti- Alzheimer's
therapies.
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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
- Programa Institucional de Fomento a la Investigacion, Desarrollo e Innovacion, Universidad Tecnologica Metropolitana, Ignacio Valdivieso 2409, P.O. Box 8940577, San Joaquin, Santiago, Chile
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10
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Wang J, Peng W, Li X, Fan W, Wei D, Wu B, Fan L, Wu C, Li L. Towards to potential 2-cyano-pyrimidines cathepsin-K inhibitors: An in silico design and screening research based on comprehensive application of quantitative structure–activity relationships, molecular docking and ADMET prediction. J Mol Struct 2019. [DOI: 10.1016/j.molstruc.2019.06.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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11
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Yang X, Wang Y, Byrne R, Schneider G, Yang S. Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery. Chem Rev 2019; 119:10520-10594. [PMID: 31294972 DOI: 10.1021/acs.chemrev.8b00728] [Citation(s) in RCA: 342] [Impact Index Per Article: 68.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development of innovative drugs. Various machine learning approaches have recently (re)emerged, some of which may be considered instances of domain-specific AI which have been successfully employed for drug discovery and design. This review provides a comprehensive portrayal of these machine learning techniques and of their applications in medicinal chemistry. After introducing the basic principles, alongside some application notes, of the various machine learning algorithms, the current state-of-the art of AI-assisted pharmaceutical discovery is discussed, including applications in structure- and ligand-based virtual screening, de novo drug design, physicochemical and pharmacokinetic property prediction, drug repurposing, and related aspects. Finally, several challenges and limitations of the current methods are summarized, with a view to potential future directions for AI-assisted drug discovery and design.
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Affiliation(s)
- Xin Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Yifei Wang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
| | - Ryan Byrne
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Gisbert Schneider
- ETH Zurich , Department of Chemistry and Applied Biosciences , Vladimir-Prelog-Weg 4 , CH-8093 Zurich , Switzerland
| | - Shengyong Yang
- State Key Laboratory of Biotherapy and Cancer Center, West China Hospital , Sichuan University , Chengdu , Sichuan 610041 , China
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Nocedo-Mena D, Cornelio C, Camacho-Corona MDR, Garza-González E, Waksman de Torres N, Arrasate S, Sotomayor N, Lete E, González-Díaz H. Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks. J Chem Inf Model 2019; 59:1109-1120. [PMID: 30802402 DOI: 10.1021/acs.jcim.9b00034] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN s) is an important goal due to the different susceptibility to antibiotics. The ChEMBL database contains >160 000 outcomes of preclinical assays of antimicrobial activity for 55 931 compounds with >365 parameters of activity (MIC, IC50, etc.) and >90 bacteria strains of >25 bacterial species. In addition, the Leong and Barabàsi data set includes >40 MRNs of microorganisms. However, there are no models able to predict antibacterial activity for multiple assays considering both drug and MRN structures at the same time. In this work, we combined perturbation theory, machine learning, and information fusion techniques to develop the first PTMLIF model. The best linear model found presented values of specificity = 90.31/90.40 and sensitivity = 88.14/88.07 in training/validation series. We carried out a comparison to nonlinear artificial neural network (ANN) techniques and previous models from the literature. Next, we illustrated the practical use of the model with an experimental case of study. We reported for the first time the isolation and characterization of terpenes from the plant Cissus incisa. The antibacterial activity of the terpenes was experimentally determined. The more active compounds were phytol and α-amyrin, with MIC = 100 μg/mL for Vancomycin-resistant Enterococcus faecium and Acinetobacter baumannii resistant to carbapenems. These compounds are already known from other sources. However, they have been isolated and evaluated for the first time here against several strains of multidrug-resistant bacteria including World Health Organization (WHO) priority pathogens. Last, we used the model to predict the activity of these compounds versus other microorganisms with different MRNs in order to find other potential targets.
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Affiliation(s)
- Deyani Nocedo-Mena
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain.,Facultad de Ciencias Químicas , Universidad Autónoma de Nuevo León , CP 66455 San Nicolás de los Garza , Nuevo León , México
| | - Carlos Cornelio
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - María Del Rayo Camacho-Corona
- Facultad de Ciencias Químicas , Universidad Autónoma de Nuevo León , CP 66455 San Nicolás de los Garza , Nuevo León , México
| | - Elvira Garza-González
- Servicio de Gastroenterología, Hospital Universitario, Dr. Eleuterio González , Universidad Autónoma de Nuevo León , CP 64460 Monterrey , Nuevo León , México
| | - Noemi Waksman de Torres
- Facultad de Medicina , Universidad Autónoma de Nuevo León , CP 64460 Monterrey , Nuevo León , México
| | - Sonia Arrasate
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Nuria Sotomayor
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Esther Lete
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain
| | - Humbert González-Díaz
- Department of Organic Chemistry II , University of the Basque Country UPV/EHU , 48940 Leioa , Spain.,IKERBASQUE, Basque Foundation for Science , 48011 Bilbao , Biscay , Spain
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13
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Wang JL, Li L, Hu MB, Wu B, Fan WX, Peng W, Wei DN, Wu CJ. In silico drug design of inhibitor of nuclear factor kappa B kinase subunit beta inhibitors from 2-acylamino-3-aminothienopyridines based on quantitative structure–activity relationships and molecular docking. Comput Biol Chem 2019; 78:297-305. [DOI: 10.1016/j.compbiolchem.2018.12.021] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 12/11/2018] [Accepted: 12/26/2018] [Indexed: 11/17/2022]
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14
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Wang J, Zhao C, Tu J, Yang H, Zhang X, Lv W, Zhai H. Design of novel quinoline-aminopiperidine derivatives as Mycobacterium tuberculosis (MTB) GyrB inhibitors: an in silico study. J Biomol Struct Dyn 2018; 37:2913-2925. [DOI: 10.1080/07391102.2018.1498806] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Juan Wang
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, PR China
| | - Chenxi Zhao
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, PR China
| | - Jing Tu
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, PR China
| | - Hong Yang
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, PR China
| | - Xiaoyun Zhang
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, PR China
| | - Wenjuan Lv
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, PR China
| | - Honglin Zhai
- College of Chemistry and Chemical Engineering, Lanzhou University, Lanzhou, PR China
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15
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Liu J, Ming B, Gong GH, Wang D, Bao GL, Yu LJ. Current research on anti-breast cancer synthetic compounds. RSC Adv 2018. [DOI: 10.1039/c7ra12912b] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Breast cancer (BC) is the most common cancer for females and its incidence tends to increase year by year.
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Affiliation(s)
- Jia Liu
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Bian Ming
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Guo-Hua Gong
- First Clinical Medical of Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
| | - Di Wang
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Gui-Lan Bao
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
| | - Li-Jun Yu
- Medicinal Chemistry and Pharmacology Institute
- Inner Mongolia University for Nationalities
- Tongliao
- People's Republic of China
- Inner Mongolia Key Laboratory of Mongolian Medicine Pharmacology for Cardio-Cerebral Vascular System
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16
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Antanasijević D, Antanasijević J, Trišović N, Ušćumlić G, Pocajt V. From Classification to Regression Multitasking QSAR Modeling Using a Novel Modular Neural Network: Simultaneous Prediction of Anticonvulsant Activity and Neurotoxicity of Succinimides. Mol Pharm 2017; 14:4476-4484. [PMID: 29130688 DOI: 10.1021/acs.molpharmaceut.7b00582] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Succinimides, which contain a pharmacophore responsible for anticonvulsant activity, are frequently used antiepileptic drugs and the synthesis of their new derivatives with improved efficacy and tolerability presents an important task. Nowadays, multitarget/tasking methodologies focused on quantitative-structure activity relationships (mt-QSAR/mtk-QSAR) have an important role in the rational design of drugs since they enable simultaneous prediction of several standard measures of biological activities at diverse experimental conditions and against different biological targets. Relating to this very topic, the mt-QSAR/mtk-QSAR methodology can give only binary classification models, and as such, in this study a regression mtk-QSAR (rmtk-QSAR) model based on a novel modular neural network (MNN) has been proposed. The MNN uses standard classification mtk-QSAR models as input modules, while the regression is performed by the output module. The rmtk-QSAR model has been successfully developed for the simultaneous prediction of anticonvulsant activity and neurotoxicity of succinimides, with a satisfactory accuracy in testing (R2 = 0.87). Thus, the proposed mtk-QSAR regression method can be regarded as a viable alternative to the standard QSAR methodology.
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Affiliation(s)
- Davor Antanasijević
- Innovation Center of the Faculty of Technology and Metallurgy and ‡Faculty of Technology and Metallurgy, University of Belgrade , Karnegijeva 4, Belgrade 11120, Serbia
| | - Jelena Antanasijević
- Innovation Center of the Faculty of Technology and Metallurgy and ‡Faculty of Technology and Metallurgy, University of Belgrade , Karnegijeva 4, Belgrade 11120, Serbia
| | - Nemanja Trišović
- Innovation Center of the Faculty of Technology and Metallurgy and ‡Faculty of Technology and Metallurgy, University of Belgrade , Karnegijeva 4, Belgrade 11120, Serbia
| | - Gordana Ušćumlić
- Innovation Center of the Faculty of Technology and Metallurgy and ‡Faculty of Technology and Metallurgy, University of Belgrade , Karnegijeva 4, Belgrade 11120, Serbia
| | - Viktor Pocajt
- Innovation Center of the Faculty of Technology and Metallurgy and ‡Faculty of Technology and Metallurgy, University of Belgrade , Karnegijeva 4, Belgrade 11120, Serbia
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17
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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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18
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Chitre TS, Asgaonkar KD, Patil SM, Kumar S, Khedkar VM, Garud DR. QSAR, docking studies of 1,3-thiazinan-3-yl isonicotinamide derivatives for antitubercular activity. Comput Biol Chem 2017; 68:211-218. [PMID: 28411471 DOI: 10.1016/j.compbiolchem.2017.03.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2016] [Revised: 03/17/2017] [Accepted: 03/28/2017] [Indexed: 11/24/2022]
Abstract
The enzyme - enoyl acyl carrier protein reductase (enoyl ACP reductase) is a validated target for antitubercular activity. Inhibition of this enzyme interferes with mycolic acid synthesis which is crucial for Mycobacterium tuberculosis cell growth. In the present work 2D and 3D quantitative structure activity relationship (QSAR) studies were carried out on a series of thiazinan-Isoniazid pharmacophore to design newer analogues. For 2D QSAR, the best statistical model was generated using SA-MLR method (r2=0.958, q2=0.922) while 3D QSAR model was derived using the SA KNN method (q2=0.8498). These studies could guide the topological, electrostatic, steric, hydrophobic substitutions around the nucleus based on which the NCEs were designed. Furthermore, molecular docking was performed to gauze the binding affinity of the designed analogues for enoyl ACP reductase enzyme. Amongst all the designed analogues the binding energies of SKS 01 and SKS 05 were found to be -5.267kcal/mol and -5.237kcal/mol respectively which was comparable with the binding energy of the standard Isoniazid (-6.254kcal/mol).
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Affiliation(s)
- Trupti S Chitre
- Department of Pharmaceutical Chemistry, All India Shri Shivaji Memorial Society's College of Pharmacy, Kennedy Road, Near R.T.O., Pune 411001, M.S., India.
| | - Kalyani D Asgaonkar
- Department of Pharmaceutical Chemistry, All India Shri Shivaji Memorial Society's College of Pharmacy, Kennedy Road, Near R.T.O., Pune 411001, M.S., India
| | - Shital M Patil
- Department of Pharmaceutical Chemistry, All India Shri Shivaji Memorial Society's College of Pharmacy, Kennedy Road, Near R.T.O., Pune 411001, M.S., India
| | - Shiva Kumar
- Department of Pharmaceutical Chemistry, All India Shri Shivaji Memorial Society's College of Pharmacy, Kennedy Road, Near R.T.O., Pune 411001, M.S., India
| | - Vijay M Khedkar
- Paris Saclay University, U892 INRA, 78350 Jouy en Josas, France
| | - Dinesh R Garud
- Department of Chemistry, Sir Parashurambhau College, Affiliated to Savitribai Phule Pune University, Tilak Road, Pune 411030, India
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19
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Borsari C, Ferrari S, Venturelli A, Costi MP. Target-based approaches for the discovery of new antimycobacterial drugs. Drug Discov Today 2017; 22:576-584. [DOI: 10.1016/j.drudis.2016.11.014] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 11/07/2016] [Accepted: 11/15/2016] [Indexed: 12/24/2022]
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20
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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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21
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Vázquez-Prieto S, Paniagua E, Ubeira FM, González-Díaz H. QSPR-Perturbation Models for the Prediction of B-Epitopes from Immune Epitope Database: A Potentially Valuable Route for Predicting “In Silico” New Optimal Peptide Sequences and/or Boundary Conditions for Vaccine Development. Int J Pept Res Ther 2016. [DOI: 10.1007/s10989-016-9524-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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22
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Durrant JD, Amaro RE. Machine-learning techniques applied to antibacterial drug discovery. Chem Biol Drug Des 2015; 85:14-21. [PMID: 25521642 DOI: 10.1111/cbdd.12423] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2014] [Revised: 08/25/2014] [Accepted: 08/26/2014] [Indexed: 12/01/2022]
Abstract
The emergence of drug-resistant bacteria threatens to revert humanity back to the preantibiotic era. Even now, multidrug-resistant bacterial infections annually result in millions of hospital days, billions in healthcare costs, and, most importantly, tens of thousands of lives lost. As many pharmaceutical companies have abandoned antibiotic development in search of more lucrative therapeutics, academic researchers are uniquely positioned to fill the pipeline. Traditional high-throughput screens and lead-optimization efforts are expensive and labor intensive. Computer-aided drug-discovery techniques, which are cheaper and faster, can accelerate the identification of novel antibiotics, leading to improved hit rates and faster transitions to preclinical and clinical testing. The current review describes two machine-learning techniques, neural networks and decision trees, that have been used to identify experimentally validated antibiotics. We conclude by describing the future directions of this exciting field.
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Affiliation(s)
- Jacob D Durrant
- Department of Chemistry & Biochemistry and the National Biomedical Computation Resource, University of California, San Diego, La Jolla, CA, 92093, USA
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23
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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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24
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Liu Y, Buendía-Rodríguez G, Peñuelas-Rívas CG, Tan Z, Rívas-Guevara M, Tenorio-Borroto E, Munteanu CR, Pazos A, González-Díaz H. Experimental and computational studies of fatty acid distribution networks. MOLECULAR BIOSYSTEMS 2015; 11:2964-77. [DOI: 10.1039/c5mb00325c] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
A new PT-LFER model is useful for predicting a distribution network in terms of specific fatty acid distribution.
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Affiliation(s)
- Yong Liu
- Faculty of Veterinary Medicine and Animal Science
- Autonomous University of the State of Mexico
- Toluca
- Mexico
- Key Laboratory of Subtropical Agro-ecological Engineering
| | - Germán Buendía-Rodríguez
- National Center for Disciplinary Research on Animal Physiology and Breeding
- National Institute of Forestry
- Agriculture and Livestock Research
- Queretaro
- Mexico
| | | | - Zhiliang Tan
- Key Laboratory of Subtropical Agro-ecological Engineering
- Institute of Subtropical Agriculture, the Chinese Academy of Sciences
- Changsha
- P. R. China
| | - María Rívas-Guevara
- Ethnobiology and Biodiversity Research Center
- Chapingo Autonomous University
- Texcoco
- Mexico
| | - Esvieta Tenorio-Borroto
- Faculty of Veterinary Medicine and Animal Science
- Autonomous University of the State of Mexico
- Toluca
- Mexico
| | | | | | - Humberto González-Díaz
- Department of Organic Chemistry II
- Faculty of Science and Technology
- University of the Basque Country UPV/EHU
- Leioa
- Spain
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25
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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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26
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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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27
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Stavrakov G, Valcheva V, Philipova I, Doytchinova I. Design of novel camphane-based derivatives with antimycobacterial activity. J Mol Graph Model 2014; 51:7-12. [PMID: 24859319 DOI: 10.1016/j.jmgm.2014.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2013] [Revised: 04/02/2014] [Accepted: 04/21/2014] [Indexed: 01/19/2023]
Abstract
Although tuberculosis (TB) continues to be one of the leading infectious disease killers globally, it is curable and preventable. Despite the existence of safe, well tolerated and effective drugs used in the TB treatment, the interest in new entities, combinations and regimens increases during the last 10 years. Recently, we reported for a new class of anti-TB agents - camphane-based derivatives with nanomolar activity against Mycobacterium tuberculosis strains. The quantitative structure-activity relationship (QSAR) study on 12 compounds revealed several structural requirements for antimycobacterial activity: two hydrogen bond donors, two or three rings and no large branched substituents. Here, we describe the design of a set of nine novel camphane-based derivatives following these requirements. The compounds were synthesized and tested against M. tuberculosis strain H37Rv. Four of them showed activities in the nanomolar range, significantly higher than the activities in the initial set. The QSAR study based on all 21 derivatives pointed to two main structural requirements for anti-TB activity: two hydrogen bond donors and a side chain with aromatic ring.
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Affiliation(s)
- Georgi Stavrakov
- Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st., Sofia 1000, Bulgaria
| | - Violeta Valcheva
- Institute of Microbiology, Bulgarian Academy of Sciences, 26 Akad. Bonchev st., Sofia 1113, Bulgaria
| | - Irena Philipova
- Institute of Organic Chemistry, Bulgarian Academy of Sciences, 9 Acad. Bonchev st., Sofia 1113, Bulgaria
| | - Irini Doytchinova
- Faculty of Pharmacy, Medical University of Sofia, 2 Dunav st., Sofia 1000, Bulgaria.
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28
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Model for vaccine design by prediction of B-epitopes of IEDB given perturbations in peptide sequence, in vivo process, experimental techniques, and source or host organisms. J Immunol Res 2014; 2014:768515. [PMID: 24741624 PMCID: PMC3987976 DOI: 10.1155/2014/768515] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 11/17/2013] [Indexed: 11/17/2022] Open
Abstract
Perturbation methods add variation terms to a known experimental solution of one problem to approach a solution for a related problem without known exact solution. One problem of this type in immunology is the prediction of the possible action of epitope of one peptide after a perturbation or variation in the structure of a known peptide and/or other boundary conditions (host organism, biological process, and experimental assay). However, to the best of our knowledge, there are no reports of general-purpose perturbation models to solve this problem. In a recent work, we introduced a new quantitative structure-property relationship theory for the study of perturbations in complex biomolecular systems. In this work, we developed the first model able to classify more than 200,000 cases of perturbations with accuracy, sensitivity, and specificity >90% both in training and validation series. The perturbations include structural changes in >50000 peptides determined in experimental assays with boundary conditions involving >500 source organisms, >50 host organisms, >10 biological process, and >30 experimental techniques. The model may be useful for the prediction of new epitopes or the optimization of known peptides towards computational vaccine design.
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29
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Toropov AA, Toropova AP, Raska I, Leszczynska D, Leszczynski J. Comprehension of drug toxicity: software and databases. Comput Biol Med 2013; 45:20-5. [PMID: 24480159 DOI: 10.1016/j.compbiomed.2013.11.013] [Citation(s) in RCA: 42] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 11/12/2013] [Accepted: 11/18/2013] [Indexed: 10/26/2022]
Abstract
Quantitative structure-property/activity relationships (QSPRs/QSARs) are a tool (in silico) to rapidly predict various endpoints in general, and drug toxicity in particular. However, this dynamic evolution of experimental data (expansion of existing experimental data on drugs toxicity) leads to the problem of critical estimation of the data. The carcinogenicity, mutagenicity, liver effects and cardiac toxicity should be evaluated as the most important aspects of the drug toxicity. The toxicity is a multidimensional phenomenon. It is apparent that the main reasons for the increase in applications of in silico prediction of toxicity include the following: (i) the need to reduce animal testing; (ii) computational models provide reliable toxicity prediction; (iii) development of legislation that is related to use of new substances; (iv) filling data gaps; (v) reduction of cost and time; (vi) designing of new compounds; (vii) advancement of understanding of biology and chemistry. This mini-review provides analysis of existing databases and software which are necessary for use of robust computational assessments and robust prediction of potential drug toxicities by means of in silico methods.
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Affiliation(s)
- Andrey A Toropov
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, Milano 20156, Italy.
| | - Alla P Toropova
- IRCCS, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Via La Masa 19, Milano 20156, Italy
| | - Ivan Raska
- 3rd Department of Medicine, Department of Endocrinology and Metabolism, First Faculty of Medicine, Charles University in Prague and General University Hospital in Prague, U Nemocnice 1, 12808 Prague 2, Czech Republic
| | - Danuta Leszczynska
- Interdisciplinary Nanotoxicity Center, Department of Civil and Environmental Engineering, Jackson State University, 1325 Lynch St, Jackson, MS 39217-0510, USA
| | - Jerzy Leszczynski
- Interdisciplinary Nanotoxicity Center, Department of Chemistry and Biochemistry, Jackson State University, 1400 J. R. Lynch Street, PO Box 17910, Jackson, MS 39217, USA
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30
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Alonso N, Caamaño O, Romero-Duran FJ, Luan F, D. S. Cordeiro MN, Yañez M, González-Díaz H, García-Mera X. Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates. ACS Chem Neurosci 2013; 4:1393-403. [PMID: 23855599 DOI: 10.1021/cn400111n] [Citation(s) in RCA: 47] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.
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Affiliation(s)
- Nerea Alonso
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Olga Caamaño
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Francisco J. Romero-Duran
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
| | - Feng Luan
- REQUIMTE/Department of Chemistry
and Biochemistry, University of Porto,
4169-007, Porto, Portugal
- Department of Applied Chemistry, Yantai University, Yantai 264005, People’s Republic
of China
| | | | - Matilde Yañez
- Department of
Pharmacology,
Faculty of Pharmacy, USC, 15782, Santiago
de Compostela, Spain
| | - Humberto González-Díaz
- Departament
of Organic Chemistry
II, University of the Basque Country UPV/EHU, 48940, Leioa, Spain
- IKERBASQUE, Basque Foundation for Science, 48011, Bilbao, Spain
| | - Xerardo García-Mera
- Department of Organic Chemistry,
Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782, Santiago de Compostela, Spain
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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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