DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.

ABSTRACT: Accurate prediction of drug responses in cancer cell lines (CCLs) and transferable prediction of clinical drug responses using CCLs are two major tasks in personalized medicine. Despite the rapid advancements in existing computational methods for preclinical and clinical cancer drug response (CDR) prediction, challenges remain regarding the generalization of new drugs that are unseen in the training set. Herein, we propose a multimodal fusion deep learning (DL) model called drug-target and single-cell language based CDR (DTLCDR) to predict preclinical and clinical CDRs. The model integrates chemical descriptors, molecular graph representations, predicted protein target profiles of drugs, and cell line expression profiles with general knowledge from single cells. Among these features, a wel

SUBMITTER: Yu J 

PROVIDER: S-EPMC12491712 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature