biostudies-literatureZhang YKey Technologies Research and Development ProgramNational Natural Science Foundation of ChinaNational Key Research and Development Program of China101313https://www.ebi.ac.uk/biostudies/studies/S-EPMC12409376biostudies-literatureUnknown15(8)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 multimodJournal of pharmaceutical analysisA multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors.PMC124093762022YFF120300382373791Wu JZhang YHou TKang YfalseA multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors.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 multimod2025-01-01T00:00:00Z2025 Aug2026-06-03T07:08:07.345Z2026-05-29T03:05:47.718ZS-EPMC124093764091958710.1016/j.jpha.2025.101313