Deep learning in image-based phenotypic drug discovery - Archive ouverte HAL Access content directly
Journal Articles Trends in Cell Biology Year : 2023

Deep learning in image-based phenotypic drug discovery

(1, 2) , (2, 3) , (1, 2)
1
2
3

Abstract

Modern drug discovery approaches often use high-content imaging to systematically study the effect on cells of large libraries of chemical compounds. By automatically screening thousands or millions of images to identify specific drug-induced cellular phenotypes, for example, altered cellular morphology, these approaches can reveal ‘hit’ compounds offering therapeutic promise. In the past few years, artificial intelligence (AI) methods based on deep learning (DL) [a family of machine learning (ML) techniques] have disrupted virtually all image analysis tasks, from image classification to segmentation. These powerful methods also promise to impact drug discovery by accelerating the identification of effective drugs and their modes of action. In this review, we highlight applications and adaptations of ML, especially DL methods for cell-based phenotypic drug discovery (PDD).
Not file

Dates and versions

pasteur-03932169 , version 1 (10-01-2023)

Identifiers

Cite

Daniel Krentzel, Spencer Shorte, Christophe Zimmer. Deep learning in image-based phenotypic drug discovery. Trends in Cell Biology, In press, ⟨10.1016/j.tcb.2022.11.011⟩. ⟨pasteur-03932169⟩
0 View
0 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More