Machine Learning guided early drug discovery of small molecules.

Autor: Pillai N; Quantitative Pharmacology, DMPK, Sanofi US, Waltham, MA, USA. Electronic address: nikhil.pillai@sanofi.com., Dasgupta A; Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA., Sudsakorn S; DMPK, Sanofi US, Waltham, MA, USA., Fretland J; DMPK, Sanofi US, Waltham, MA, USA., Mavroudis PD; Quantitative Pharmacology, DMPK, Sanofi US, Waltham, MA, USA.
Jazyk: angličtina
Zdroj: Drug discovery today [Drug Discov Today] 2022 Aug; Vol. 27 (8), pp. 2209-2215. Date of Electronic Publication: 2022 Mar 29.
DOI: 10.1016/j.drudis.2022.03.017
Abstrakt: Machine learning (ML) approaches have been widely adopted within the early stages of the drug discovery process, particularly within the context of small-molecule drug candidates. Despite this, the use of ML is still limited in the pharmacokinetic/pharmacodynamic (PK/PD) application space. Here, we describe recent progress and the role of ML used in preclinical drug discovery. We summarize the advances and current strategies used to predict ADME (absorption, distribution, metabolism and, excretion) properties of small molecules based on their structures, and predict structures based on the desired properties for molecular screening and optimization. Finally, we discuss the use of ML to predict PK to rank the ability of drug candidates to achieve appropriate exposures and hence provide important insights into safety and efficacy.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)
Databáze: MEDLINE
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