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Meng L, Zhou B, Liu H, Chen Y, Yuan R, Chen Z, Luo S, Chen H. Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174201. [PMID: 38936709 DOI: 10.1016/j.scitotenv.2024.174201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 06/17/2024] [Accepted: 06/20/2024] [Indexed: 06/29/2024]
Abstract
Perfluorinated and perfluoroalkyl substances (PFASs), encompassing a vast array of isomeric chemicals, are recognized as typical emerging contaminants with direct or potential impacts on human health and the ecological environment. With the complex and elusive toxicological profiles of PFASs, machine learning (ML) has been increasingly employed in their toxicity studies due to its proficiency in prediction and data analytics. This integration is poised to become a predominant trend in environmental toxicology, propelled by the swift advancements in computational technology. This review diligently examines the literature to encapsulate the varied objectives of employing ML in the toxicity studies of PFASs: (1) Utilizing ML to establish Quantitative Structure-Activity Relationship (QSAR) models for PFASs with diverse toxicity endpoints, facilitating the targeted toxicity prediction of unidentified PFASs; (2) Investigating and substantiating the Adverse Outcome Pathway (AOP) through the synergy of ML and traditional toxicological methods, with this refining the toxicity assessment framework for PFASs; (3) Dissecting and elucidating the features of established ML models to advance Open Research into the toxicity of PFASs, with a primary focus on determinants and mechanisms. The discourse extends to an in-depth examination of ML studies, segregating findings based on their distinct application trajectories. Given that ML represents a nascent paradigm within PFASs research, this review delineates the collective challenges encountered in the ML-mediated study of PFAS toxicity and proffers strategic guidance for ensuing investigations.
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Affiliation(s)
- Lingxuan Meng
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Beihai Zhou
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Haijun Liu
- School of Resources and Environment, Anqing Normal University, Anqing, China.
| | - Yuefang Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
| | - Rongfang Yuan
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Zhongbing Chen
- Faculty of Environmental Sciences, Czech University of Life Sciences Prague, Kamýcká 129, 16500 Praha-Suchdol, Czech Republic.
| | - Shuai Luo
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China
| | - Huilun Chen
- Beijing Key Laboratory of Resource-oriented Treatment of Industrial Pollutants, School of Energy and Environmental Engineering, University of Science and Technology Beijing, Beijing 100083, China.
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AlJarf R, Rodrigues CHM, Myung Y, Pires DEV, Ascher DB. piscesCSM: prediction of anticancer synergistic drug combinations. J Cheminform 2024; 16:81. [PMID: 39030592 PMCID: PMC11264925 DOI: 10.1186/s13321-024-00859-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 05/12/2024] [Indexed: 07/21/2024] Open
Abstract
While drug combination therapies are of great importance, particularly in cancer treatment, identifying novel synergistic drug combinations has been a challenging venture. Computational methods have emerged in this context as a promising tool for prioritizing drug combinations for further evaluation, though they have presented limited performance, utility, and interpretability. Here, we propose a novel predictive tool, piscesCSM, that leverages graph-based representations to model small molecule chemical structures to accurately predict drug combinations with favourable anticancer synergistic effects against one or multiple cancer cell lines. Leveraging these insights, we developed a general supervised machine learning model to guide the prediction of anticancer synergistic drug combinations in over 30 cell lines. It achieved an area under the receiver operating characteristic curve (AUROC) of up to 0.89 on independent non-redundant blind tests, outperforming state-of-the-art approaches on both large-scale oncology screening data and an independent test set generated by AstraZeneca (with more than a 16% improvement in predictive accuracy). Moreover, by exploring the interpretability of our approach, we found that simple physicochemical properties and graph-based signatures are predictive of chemotherapy synergism. To provide a simple and integrated platform to rapidly screen potential candidate pairs with favourable synergistic anticancer effects, we made piscesCSM freely available online at https://biosig.lab.uq.edu.au/piscescsm/ as a web server and API. We believe that our predictive tool will provide a valuable resource for optimizing and augmenting combinatorial screening libraries to identify effective and safe synergistic anticancer drug combinations. SCIENTIFIC CONTRIBUTION: This work proposes piscesCSM, a machine-learning-based framework that relies on well-established graph-based representations of small molecules to identify and provide better predictive accuracy of syngenetic drug combinations. Our model, piscesCSM, shows that combining physiochemical properties with graph-based signatures can outperform current architectures on classification prediction tasks. Furthermore, implementing our tool as a web server offers a user-friendly platform for researchers to screen for potential synergistic drug combinations with favorable anticancer effects against one or multiple cancer cell lines.
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Affiliation(s)
- Raghad AlJarf
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Carlos H M Rodrigues
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Yoochan Myung
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia
| | - Douglas E V Pires
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
- School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, Australia
| | - David B Ascher
- Structural Biology and Bioinformatics, Department of Biochemistry and Pharmacology, University of Melbourne, Melbourne, VIC, Australia.
- Systems and Computational Biology, Bio21 Institute, University of Melbourne, Melbourne, VIC, Australia.
- Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia.
- School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, QLD, Australia.
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Milićević J, Petrović S, Tošić S, Vrecl M, Arsić B. Recent Computer-Aided Studies on Herbicides: A Short Review. Chem Biodivers 2024:e202400531. [PMID: 38948948 DOI: 10.1002/cbdv.202400531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2024] [Revised: 06/28/2024] [Accepted: 07/01/2024] [Indexed: 07/02/2024]
Abstract
Current industrial herbicides have a negative impact on the environment and have widespread resistance, so computational studies on their properties, elimination, and overcoming resistance can be helpful. On the other hand, developing new herbicides, especially bioherbicides, is slow and costly. Therefore, computational studies that guide the design and search for new herbicides that exist in various plant sources, can alleviate the pain associated with the many obstacles. This review summarizes for the first time the most recent studies on both aspects of herbicides over 10 years.
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Affiliation(s)
- Jelena Milićević
- Laboratory for Bioinformatics and Computational Chemistry, Vinča Institute of Nuclear Sciences, University of Belgrade, Mike Petrovića Alasa 12-14, 11351, Vinča, Belgrade, Republic of Serbia
| | - Stefan Petrović
- Department of Chemistry, Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18106, Niš, Republic of Serbia
| | - Snežana Tošić
- Department of Chemistry, Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18106, Niš, Republic of Serbia
| | - Milka Vrecl
- Institute for Preclinical Sciences, Veterinary Faculty, University of Ljubljana, Gerbičeva ulica 60, 1000, Ljubljana, Slovenia
| | - Biljana Arsić
- Department of Chemistry, Faculty of Sciences and Mathematics, University of Niš, Višegradska 33, 18106, Niš, Republic of Serbia
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Velloso JPL, de Sá AGC, Pires DEV, Ascher DB. Engineering G protein-coupled receptors for stabilization. Protein Sci 2024; 33:e5000. [PMID: 38747401 PMCID: PMC11094779 DOI: 10.1002/pro.5000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Revised: 03/21/2024] [Accepted: 04/10/2024] [Indexed: 05/19/2024]
Abstract
G protein-coupled receptors (GPCRs) are one of the most important families of targets for drug discovery. One of the limiting steps in the study of GPCRs has been their stability, with significant and time-consuming protein engineering often used to stabilize GPCRs for structural characterization and drug screening. Unfortunately, computational methods developed using globular soluble proteins have translated poorly to the rational engineering of GPCRs. To fill this gap, we propose GPCR-tm, a novel and personalized structurally driven web-based machine learning tool to study the impacts of mutations on GPCR stability. We show that GPCR-tm performs as well as or better than alternative methods, and that it can accurately rank the stability changes of a wide range of mutations occurring in various types of class A GPCRs. GPCR-tm achieved Pearson's correlation coefficients of 0.74 and 0.46 on 10-fold cross-validation and blind test sets, respectively. We observed that the (structural) graph-based signatures were the most important set of features for predicting destabilizing mutations, which points out that these signatures properly describe the changes in the environment where the mutations occur. More specifically, GPCR-tm was able to accurately rank mutations based on their effect on protein stability, guiding their rational stabilization. GPCR-tm is available through a user-friendly web server at https://biosig.lab.uq.edu.au/gpcr_tm/.
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Affiliation(s)
- João Paulo L. Velloso
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Alex G. C. de Sá
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
| | - Douglas E. V. Pires
- School of Computing and Information SystemsThe University of MelbourneParkvilleVictoriaAustralia
| | - David B. Ascher
- School of Chemistry and Molecular Biosciences, The Australian Centre for EcogenomicsThe University of QueenslandBrisbaneQueenslandAustralia
- Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
- Baker Department of Cardiometabolic HealthThe University of MelbourneParkvilleVictoriaAustralia
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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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6
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Berestetskiy A. Modern Approaches for the Development of New Herbicides Based on Natural Compounds. PLANTS (BASEL, SWITZERLAND) 2023; 12:234. [PMID: 36678947 PMCID: PMC9864389 DOI: 10.3390/plants12020234] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Revised: 12/13/2022] [Accepted: 12/24/2022] [Indexed: 05/12/2023]
Abstract
Weeds are a permanent component of anthropogenic ecosystems. They require strict control to avoid the accumulation of their long-lasting seeds in the soil. With high crop infestation, many elements of crop production technologies (fertilization, productive varieties, growth stimulators, etc.) turn out to be practically meaningless due to high yield losses. Intensive use of chemical herbicides (CHs) has led to undesirable consequences: contamination of soil and wastewater, accumulation of their residues in the crop, and the emergence of CH-resistant populations of weeds. In this regard, the development of environmentally friendly CHs with new mechanisms of action is relevant. The natural phytotoxins of plant or microbial origin may be explored directly in herbicidal formulations (biorational CHs) or indirectly as scaffolds for nature-derived CHs. This review considers (1) the main current trends in the development of CHs that may be important for the enhancement of biorational herbicides; (2) the advances in the development and practical application of natural compounds for weed control; (3) the use of phytotoxins as prototypes of synthetic herbicides. Some modern approaches, such as computational methods of virtual screening and design of herbicidal molecules, development of modern formulations, and determination of molecular targets, are stressed as crucial to make the exploration of natural compounds more effective.
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Affiliation(s)
- Alexander Berestetskiy
- Laboratory of Phytotoxicology and Biotechnology, All-Russian Institute of Plant Protection, Pushkin, 196608 Saint-Petersburg, Russia
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Zhou Y, Al‐Jarf R, Alavi A, Nguyen TB, Rodrigues CHM, Pires DEV, Ascher DB. kinCSM: Using graph-based signatures to predict small molecule CDK2 inhibitors. Protein Sci 2022; 31:e4453. [PMID: 36305769 PMCID: PMC9597374 DOI: 10.1002/pro.4453] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Revised: 09/14/2022] [Accepted: 09/15/2022] [Indexed: 11/20/2022]
Abstract
Protein phosphorylation acts as an essential on/off switch in many cellular signaling pathways. This has led to ongoing interest in targeting kinases for therapeutic intervention. Computer‐aided drug discovery has been proven a useful and cost‐effective approach for facilitating prioritization and enrichment of screening libraries, but limited effort has been devoted providing insights on what makes a potent kinase inhibitor. To fill this gap, here we developed kinCSM, an integrative computational tool capable of accurately identifying potent cyclin‐dependent kinase 2 (CDK2) inhibitors, quantitatively predicting CDK2 ligand–kinase inhibition constants (pKi) and classifying different types of inhibitors based on their favorable binding modes. kinCSM predictive models were built using supervised learning and leveraged the concept of graph‐based signatures to capture both physicochemical properties and geometry properties of small molecules. CDK2 inhibitors were accurately identified with Matthew's Correlation Coefficients (MCC) of up to 0.74, and inhibition constants predicted with Pearson's correlation of up to 0.76, both with consistent performances of 0.66 and 0.68 on a nonredundant blind test, respectively. kinCSM was also able to identify the potential type of inhibition for a given molecule, achieving MCC of up to 0.80 on cross‐validation and 0.73 on the blind test. Analyzing the molecular composition of revealed enriched chemical fragments in CDK2 inhibitors and different types of inhibitors, which provides insights into the molecular mechanisms behind ligand–kinase interactions. kinCSM will be an invaluable tool to guide future kinase drug discovery. To aid the fast and accurate screening of CDK2 inhibitors, kinCSM is freely available at https://biosig.lab.uq.edu.au/kin_csm/.
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Affiliation(s)
- Yunzhuo Zhou
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Raghad Al‐Jarf
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Azadeh Alavi
- Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Thanh Binh Nguyen
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Carlos H. M. Rodrigues
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
| | - Douglas E. V. Pires
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia,School of Computing and Information SystemsUniversity of MelbourneMelbourneVictoriaAustralia
| | - David B. Ascher
- School of Chemistry and Molecular BiosciencesUniversity of QueenslandBrisbaneQueenslandAustralia,Structural Biology and Bioinformatics, Department of BiochemistryUniversity of MelbourneMelbourneVictoriaAustralia,Systems and Computational Biology, Bio21 InstituteUniversity of MelbourneMelbourneVictoriaAustralia,Computational Biology and Clinical InformaticsBaker Heart and Diabetes InstituteMelbourneVictoriaAustralia
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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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9
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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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