A multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors.

ABSTRACT: P-glycoprotein (P-gp) is a transmembrane protein widely involved in the absorption, distribution, metabolism, excretion, and toxicity (ADMET) of drugs within the human body. Accurate prediction of P-gp inhibitors and substrates is crucial for drug discovery and toxicological assessment. However, existing models rely on limited molecular information, leading to suboptimal model performance for predicting P-gp inhibitors and substrates. To overcome this challenge, we compiled an extensive dataset from public databases and literature, consisting of 5,943 P-gp inhibitors and 4,018 substrates, notable for their high quantity, quality, and structural uniqueness. In addition, we curated two external test sets to validate the model's generalization capability. Subsequently, we developed a multimod

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12409376 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature