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• W4223889031 abstract "This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug-drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non-linear datasets, as well as big data of increasing depth and complexity. Various machine learning-based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand-based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved." @default.
• W4223889031 created "2022-04-19" @default.
• W4223889031 creator A5034732406 @default.
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• W4223889031 creator A5054553587 @default.
• W4223889031 creator A5066974016 @default.
• W4223889031 creator A5069945617 @default.
• W4223889031 date "2022-04-23" @default.
• W4223889031 modified "2023-10-16" @default.
• W4223889031 title "Machine learning approaches and their applications in drug discovery and design" @default.
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• W4223889031 doi "https://doi.org/10.1111/cbdd.14057" @default.
• W4223889031 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35426249" @default.
• W4223889031 hasPublicationYear "2022" @default.
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