Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.

Autor: Zhao L; The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA., Ciallella HL; The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA., Aleksunes LM; Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA., Zhu H; The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA. Electronic address: hao.zhu99@rutgers.edu.
Jazyk: angličtina
Zdroj: Drug discovery today [Drug Discov Today] 2020 Sep; Vol. 25 (9), pp. 1624-1638. Date of Electronic Publication: 2020 Jul 11.
DOI: 10.1016/j.drudis.2020.07.005
Abstrakt: Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.
(Copyright © 2020 Elsevier Ltd. All rights reserved.)
Databáze: MEDLINE
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