Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling.
Journal
Drug discovery today
ISSN: 1878-5832
Titre abrégé: Drug Discov Today
Pays: England
ID NLM: 9604391
Informations de publication
Date de publication:
09 2020
09 2020
Historique:
received:
19
02
2020
revised:
26
06
2020
accepted:
06
07
2020
pubmed:
15
7
2020
medline:
10
9
2021
entrez:
15
7
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32663517
pii: S1359-6446(20)30264-6
doi: 10.1016/j.drudis.2020.07.005
pmc: PMC7572559
mid: NIHMS1611617
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1624-1638Subventions
Organisme : NIEHS NIH HHS
ID : P30 ES005022
Pays : United States
Organisme : NIEHS NIH HHS
ID : R01 ES031080
Pays : United States
Organisme : NIEHS NIH HHS
ID : R15 ES023148
Pays : United States
Informations de copyright
Copyright © 2020 Elsevier Ltd. All rights reserved.
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