DrugDL: Dual-modal deep learning framework for multi-property drug prediction and targeted therapy discovery

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ID: 317297
2026
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Abstract
Abstract Motivation The accurate and robust representation of drug molecule features, the prediction of drug-target biomacromolecule interactions, and the determination of physicochemical properties are crucial in drug development. However, these tasks remain challenging due to issues such as the limited generalizability of single-modal representations, the absence of multitask prediction frameworks, and weak adaptability in cold-start scenarios. Results In this study, we present DrugDL, a framework for comprehensive drug molecule representation and the prediction of multiple downstream tasks, including drug-target interactions, binding affinities, binding sites, physicochemical properties, toxicity, and drug–drug interactions. DrugDL jointly learns representations of the drug chemical space and the target protein biological space, while capturing multiscale interaction mechanisms between drug molecules and target proteins through the integration of cross-modal contrastive learning and single-modal feature enhancement algorithms. Specifically, DrugDL employs a multitask prediction framework to predict multiple properties of drug molecules. In practical applications, it consistently outperforms state-of-the-art methods, particularly in cold-start tasks. The framework has been successfully applied to high-throughput screening, the identification of inhibitors of SARS-CoV-2 and metabolic enzymes, and the prediction of cancer-targeted drugs. Experimental validations on EGFR and ALK targets further demonstrate its effectiveness as a precise drug discovery tool. By enabling accurate molecular representation and multi-property prediction, DrugDL provides end-to-end technical support for drug development, thereby significantly accelerating the drug discovery process. Availability and implementation The datasets and code are available at https://github.com/ZhangQi9910/DrugDL. The version of record is archived in Zenodo with the DOI: 10.5281/zenodo.20579718. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7164842683 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Q Zhang, Xuan Yu, Yuxiao Wei, Yunpeng Xia, Long-Chen Shen, Zhi‐Hui Wang, Hong-Bin Shen, Dong‐Jun Yu
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag392
URL
https://doi.org/10.1093/bioinformatics/btag392
Keywords Keywords not found

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