Artificial intelligence for high content imaging in drug discovery.
Journal
Current opinion in structural biology
ISSN: 1879-033X
Titre abrégé: Curr Opin Struct Biol
Pays: England
ID NLM: 9107784
Informations de publication
Date de publication:
25 May 2024
25 May 2024
Historique:
received:
26
02
2024
revised:
28
04
2024
accepted:
29
04
2024
medline:
27
5
2024
pubmed:
27
5
2024
entrez:
26
5
2024
Statut:
aheadofprint
Résumé
Artificial intelligence (AI) and high-content imaging (HCI) are contributing to advancements in drug discovery, propelled by the recent progress in deep neural networks. This review highlights AI's role in analysis of HCI data from fixed and live-cell imaging, enabling novel label-free and multi-channel fluorescent screening methods, and improving compound profiling. HCI experiments are rapid and cost-effective, facilitating large data set accumulation for AI model training. However, the success of AI in drug discovery also depends on high-quality data, reproducible experiments, and robust validation to ensure model performance. Despite challenges like the need for annotated compounds and managing vast image data, AI's potential in phenotypic screening and drug profiling is significant. Future improvements in AI, including increased interpretability and integration of multiple modalities, are expected to solidify AI and HCI's role in drug discovery.
Identifiants
pubmed: 38797109
pii: S0959-440X(24)00069-1
doi: 10.1016/j.sbi.2024.102842
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
102842Informations de copyright
Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of competing interest OS and JCP declare ownership in Phenaros Pharmaceuticals AB, a company exploiting AI, automation and HCI for drug discovery.