Natural product drug discovery in the artificial intelligence era.
Journal
Chemical science
ISSN: 2041-6520
Titre abrégé: Chem Sci
Pays: England
ID NLM: 101545951
Informations de publication
Date de publication:
09 Feb 2022
09 Feb 2022
Historique:
received:
13
08
2021
accepted:
10
12
2021
entrez:
14
3
2022
pubmed:
15
3
2022
medline:
15
3
2022
Statut:
epublish
Résumé
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
Identifiants
pubmed: 35282622
doi: 10.1039/d1sc04471k
pii: d1sc04471k
pmc: PMC8827052
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
1526-1546Informations de copyright
This journal is © The Royal Society of Chemistry.
Déclaration de conflit d'intérêts
There are no conflicts to declare.
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