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Abstract
The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.
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Affiliation(s)
- Adam Serghini
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Stephanie Portelli
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia, QLD, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
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Dixit R, Khambhati K, Supraja KV, Singh V, Lederer F, Show PL, Awasthi MK, Sharma A, Jain R. Application of machine learning on understanding biomolecule interactions in cellular machinery. BIORESOURCE TECHNOLOGY 2023; 370:128522. [PMID: 36565819 DOI: 10.1016/j.biortech.2022.128522] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Machine learning (ML) applications have become ubiquitous in all fields of research including protein science and engineering. Apart from protein structure and mutation prediction, scientists are focusing on knowledge gaps with respect to the molecular mechanisms involved in protein binding and interactions with other components in the experimental setups or the human body. Researchers are working on several wet-lab techniques and generating data for a better understanding of concepts and mechanics involved. The information like biomolecular structure, binding affinities, structure fluctuations and movements are enormous which can be handled and analyzed by ML. Therefore, this review highlights the significance of ML in understanding the biomolecular interactions while assisting in various fields of research such as drug discovery, nanomedicine, nanotoxicity and material science. Hence, the way ahead would be to force hand-in hand of laboratory work and computational techniques.
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Affiliation(s)
- Rewati Dixit
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Haus-khas, New Delhi 110016, India
| | - Khushal Khambhati
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana 382715, Gujarat, India
| | - Kolli Venkata Supraja
- Waste Treatment Laboratory, Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Haus-khas, New Delhi 110016, India
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana 382715, Gujarat, India
| | - Franziska Lederer
- Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Bautzner landstrasse 400, 01328 Dresden, Germany
| | - Pau-Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India; Department of Chemical and Environmental Engineering, University of Nottingham, Malaysia, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China
| | - Abhinav Sharma
- Institute Theory of Polymers, Leibniz Institute for Polymer Research, Hohe Strasse 6, 01069 Dresden, Germany
| | - Rohan Jain
- Helmholtz-Zentrum Dresden-Rossendorf, Helmholtz Institute Freiberg for Resource Technology, Bautzner landstrasse 400, 01328 Dresden, Germany.
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Iftkhar S, de Sá AGC, Velloso JPL, Aljarf R, Pires DEV, Ascher DB. cardioToxCSM: A Web Server for Predicting Cardiotoxicity of Small Molecules. J Chem Inf Model 2022; 62:4827-4836. [PMID: 36219164 DOI: 10.1021/acs.jcim.2c00822] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The design of novel, safe, and effective drugs to treat human diseases is a challenging venture, with toxicity being one of the main sources of attrition at later stages of development. Failure due to toxicity incurs a significant increase in costs and time to market, with multiple drugs being withdrawn from the market due to their adverse effects. Cardiotoxicity, for instance, was responsible for the failure of drugs such as fenspiride, propoxyphene, and valdecoxib. While significant effort has been dedicated to mitigate this issue by developing computational approaches that aim to identify molecules likely to be toxic, including quantitative structure-activity relationship models and machine learning methods, current approaches present limited performance and interpretability. To overcome these, we propose a new web-based computational method, cardioToxCSM, which can predict six types of cardiac toxicity outcomes, including arrhythmia, cardiac failure, heart block, hERG toxicity, hypertension, and myocardial infarction, efficiently and accurately. cardioToxCSM was developed using the concept of graph-based signatures, molecular descriptors, toxicophore matchings, and molecular fingerprints, leveraging explainable machine learning, and was validated internally via different cross validation schemes and externally via low-redundancy blind sets. The models presented robust performances with areas under ROC curves of up to 0.898 on 5-fold cross-validation, consistent with metrics on blind tests. Additionally, our models provide interpretation of the predictions by identifying whether substructures that are commonly enriched in toxic compounds were present. We believe cardioToxCSM will provide valuable insight into the potential cardiotoxicity of small molecules early on drug screening efforts. The method is made freely available as a web server at https://biosig.lab.uq.edu.au/cardiotoxcsm.
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Affiliation(s)
- Saba Iftkhar
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
| | - Alex G C de Sá
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
| | - João P L Velloso
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia
| | - Raghad Aljarf
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville 3052, Victoria, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, St Lucia 4072, Queensland, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville 3052, Victoria, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne 3004, Victoria, Australia.,Baker Department of Cardiometabolic Health, Melbourne Medical School, University of Melbourne, Parkville 3010, Victoria, Australia
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de Sá AGC, Long Y, Portelli S, Pires DEV, Ascher DB. toxCSM: comprehensive prediction of small molecule toxicity profiles. Brief Bioinform 2022; 23:6673851. [PMID: 35998885 DOI: 10.1093/bib/bbac337] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 07/17/2022] [Accepted: 07/23/2022] [Indexed: 01/29/2023] Open
Abstract
Drug discovery is a lengthy, costly and high-risk endeavour that is further convoluted by high attrition rates in later development stages. Toxicity has been one of the main causes of failure during clinical trials, increasing drug development time and costs. To facilitate early identification and optimisation of toxicity profiles, several computational tools emerged aiming at improving success rates by timely pre-screening drug candidates. Despite these efforts, there is an increasing demand for platforms capable of assessing both environmental as well as human-based toxicity properties at large scale. Here, we present toxCSM, a comprehensive computational platform for the study and optimisation of toxicity profiles of small molecules. toxCSM leverages on the well-established concepts of graph-based signatures, molecular descriptors and similarity scores to develop 36 models for predicting a range of toxicity properties, which can assist in developing safer drugs and agrochemicals. toxCSM achieved an Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of up to 0.99 and Pearson's correlation coefficients of up to 0.94 on 10-fold cross-validation, with comparable performance on blind test sets, outperforming all alternative methods. toxCSM is freely available as a user-friendly web server and API at http://biosig.lab.uq.edu.au/toxcsm.
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Affiliation(s)
- Alex G C de Sá
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, 3010, Australia
| | - Yangyang Long
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Stephanie Portelli
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,School of Computing and Information Systems, University of Melbourne, Parkville, Victoria, 3052, Australia
| | - David B Ascher
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane City, Queensland, 4072, Australia.,Systems and Computational Biology, Bio21 Institute, University of Melbourne, Parkville, Victoria, 3052, Australia.,Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, 3004, Australia.,Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, Victoria, 3010, Australia
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