A deep learning framework for high-throughput mechanism-driven phenotype compound screening.

ABSTRACT: Target-based high-throughput compound screening dominates conventional one-drug-one-gene drug discovery process. However, the readout from the chemical modulation of a single protein is poorly correlated with phenotypic response of organism, leading to high failure rate in drug development. Chemical-induced gene expression profile provides an attractive solution to phenotype-based screening. However, the use of such data is currently limited by their sparseness, unreliability, and relatively low throughput. Several methods have been proposed to impute missing values for gene expression datasets. However, few existing methods can perform de novo chemical compound screening. In this study, we propose a mechanism-driven neural network-based method named DeepCE (Deep Chemical Expression

SUBMITTER: Pham TH 

PROVIDER: S-EPMC7386506 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature