Work

CPDP: Contrastive Protein–Drug Pre-Training for Novel Drug Discovery

Year: 2025

Type: article

Abstract: Novel drug discovery and repositioning remain critical challenges in biomedical research, requiring accurate prediction of drug-target interactions (DTIs). We propose the CPDP framework, which builds upon existing biomedical representation models and integrates contrastive learning with multi-dimensional representations of proteins and drugs to predict DTIs. By aligning the representation space, CPDP enables GNN-based methods to achieve zero-shot learning capabilities, allowing for accurate predictions of unseen drug data. This approach enhances DTI prediction performance, particularly for novel drugs not included in the BioHNs dataset. Experimental results demonstrate CPDP's high accuracy and strong generalization ability in predicting novel biological entities while maintaining effectiveness for traditional drug repositioning tasks.

Source: International Journal of Molecular Sciences

DOI: 10.3390/ijms26083761

Authors: Shihan Zhang, Xiaoqi Wang, Fei Li, Shaoliang Peng

Institutions: Hunan University, Northwestern Polytechnical University, Chinese Academy of Sciences, Computer Network Information Center, State Key Laboratory of Chemobiosensing and Chemometrics

Language: English

Topic: Computational Drug Discovery Methods

Subfield: Computational Theory and Mathematics

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,499