Deep phenotypic profiling of neuroactive drugs in larval zebrafish.

ABSTRACT: Behavioral larval zebrafish screens leverage a high-throughput small molecule discovery format to find neuroactive molecules relevant to mammalian physiology. We screen a library of 650 central nervous system active compounds in high replicate to train deep metric learning models on zebrafish behavioral profiles. The machine learning initially exploited subtle artifacts in the phenotypic screen, necessitating a complete experimental re-run with rigorous physical well-wise randomization. These large matched phenotypic screening datasets (initial and well-randomized) provide a unique opportunity to quantify and understand shortcut learning in a full-scale, real-world drug discovery dataset. The final deep metric learning model substantially outperforms correlation distance-the canonical way

SUBMITTER: Gendelev L 

PROVIDER: S-EPMC11570628 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature