1. Deep phenotypic profiling of neuroactive drugs in larval zebrafish.
- Author
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Gendelev L, Taylor J, Myers-Turnbull D, Chen S, McCarroll MN, Arkin MR, Kokel D, and Keiser MJ
- Subjects
- Animals, High-Throughput Screening Assays methods, Humans, Deep Learning, Small Molecule Libraries pharmacology, Drug Evaluation, Preclinical methods, Central Nervous System Agents pharmacology, Zebrafish genetics, Larva drug effects, Phenotype, Drug Discovery methods, Behavior, Animal drug effects
- 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 of computing distances between profiles-and generalizes to an orthogonal dataset of diverse drug-like compounds. We validate predictions by prospective in vitro radio-ligand binding assays against human protein targets, achieving a hit rate of 58% despite crossing species and chemical scaffold boundaries. These neuroactive compounds exhibit diverse chemical scaffolds, demonstrating that zebrafish phenotypic screens combined with metric learning achieve robust scaffold hopping capabilities., Competing Interests: Competing interests The authors declare no competing interests., (© 2024. The Author(s).)
- Published
- 2024
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