Deep representation learning of chemical-induced transcriptional profile for phenotype-based drug discovery.

ABSTRACT: Artificial intelligence transforms drug discovery, with phenotype-based approaches emerging as a promising alternative to target-based methods, overcoming limitations like lack of well-defined targets. While chemical-induced transcriptional profiles offer a comprehensive view of drug mechanisms, inherent noise often obscures the true signal, hindering their potential for meaningful insights. Here, we highlight the development of TranSiGen, a deep generative model employing self-supervised representation learning. TranSiGen analyzes basal cell gene expression and molecular structures to reconstruct chemical-induced transcriptional profiles with high accuracy. By capturing both cellular and compound information, TranSiGen-derived representations demonstrate efficacy in diverse downstream tas

SUBMITTER: Tong X 

PROVIDER: S-EPMC11199551 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature