AI-Driven Enhancements in Drug Screening and Optimization.
Computational biology
Drug development
In silico
Machine learning
Pharmacokinetics
Toxicity
Journal
Methods in molecular biology (Clifton, N.J.)
ISSN: 1940-6029
Titre abrégé: Methods Mol Biol
Pays: United States
ID NLM: 9214969
Informations de publication
Date de publication:
2024
2024
Historique:
medline:
8
9
2023
pubmed:
7
9
2023
entrez:
7
9
2023
Statut:
ppublish
Résumé
The greatest challenge in drug discovery remains the high rate of attrition across the different phases of the process, which cost the industry billions of dollars every year. While all phases remain crucial to ensure pharmaceutical-level safety, quality, and efficacy of the end product, streamlining these efforts toward compounds with success potential is pivotal for a more efficient and cost-effective process. The use of artificial intelligence (AI) within the pharmaceutical industry aims at just this, and has applications in preclinical screening for biological activity, optimization of pharmacokinetic properties for improved drug formulation, early toxicity prediction which reduces attrition, and pre-emptively screening for genetic changes in the biological target to improve therapeutic longevity. Here, we present a series of in silico tools that address these applications in small molecule development and describe how they can be embedded within the current pharmaceutical development pipeline.
Identifiants
pubmed: 37676604
doi: 10.1007/978-1-0716-3441-7_15
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
269-294Informations de copyright
© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.
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