DTLCDR: A target-based multimodal fusion deep learning framework for cancer drug response prediction.
10.1016/j.jpha.2025.101315
- Author:
Jie YU
1
;
Cheng SHI
1
;
Yiran ZHOU
1
;
Ningfeng LIU
1
;
Xiaolin ZONG
1
;
Zhenming LIU
1
;
Liangren ZHANG
1
Author Information
1. State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Beijing, 100191, China.
- Publication Type:Journal Article
- Keywords:
Cancer drug response;
Deep learning;
Drug-target interaction;
Multimodal fusion;
Personalized medicine;
Single-cell language model
- From:
Journal of Pharmaceutical Analysis
2025;15(8):101315-101315
- CountryChina
- Language:English
-
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 well-trained drug-target interaction (DTI) prediction model is used to generate target profiles of drugs, and a pretrained single-cell language model is integrated to provide general genomic knowledge. Comparison experiments on the cell line drug sensitivity dataset demonstrated that DTLCDR exhibited improved generalizability and robustness in predicting unseen drugs compared with previous state-of-the-art baseline methods. Further ablation studies verified the effectiveness of each component of our model, highlighting the significant contribution of target information to generalizability. Subsequently, the ability of DTLCDR to predict novel molecules was validated through in vitro cell experiments, demonstrating its potential for real-world applications. Moreover, DTLCDR was transferred to the clinical datasets, demonstrating satisfactory performance in the clinical data, regardless of whether the drugs were included in the cell line dataset. Overall, our results suggest that the DTLCDR is a promising tool for personalized drug discovery.
