DCPM-ADMET: fusion of dual-component pre-trained model and molecular fingerprints to enhance drug ADMET properties prediction.

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties of drugs are critical to their efficacy and safety in clinical trials; however, traditional machine learning methods have limited generalization ability in ADMET prediction due to insufficient data. To address this issue, we developed DCPM-ADMET, an innovative pre-trained model with higher accuracy, whose architecture employs a two-component system, an XLNet-based module for capturing the deep semantics of molecular sequences, and a specialized RNN-based component (GRU) designed to encode global molecular property descriptors into high-dimensional latent representations for robust property extraction. By further incorporating ECFP fingerprints to capture local substructures, the model outperforms traditional methods and most pre-trained models in prediction accuracy on multiple benchmark datasets for molecular properties; additionally, we fine-tuned it on a self-constructed database containing 465,470 entries covering 97 ADMET properties, and by integrating these 97 prediction models and 36 computational properties, we further developed a free online ADMET prediction tool with 133 endpoints (available at  http://admet.bioai-global.com/ ), which is designed to assist researchers in conducting comprehensive molecular ADMET predictions.Scientific contributionThe development of DCPM-ADMET provides a robust and effective framework for molecular property prediction in computational pharmacology. Our architecture innovatively employs a dual-component system: an XLNet-based module for deep capture of molecular sequence semantics, a multi-task GRU module for joint SMILES translation and physicochemical property descriptor regression. Furthermore, we incorporate ECFP fingerprints to achieve exhaustive substructural feature encoding. Leveraging this multimodal fusion strategy, DCPM-ADMET exhibits superior predictive performance across diverse molecular property benchmark datasets, outperforming both traditional fingerprinting methods and state-of-the-art pre-trained models. Subsequently, we fine-tuned the model on a self-developed proprietary database-currently the largest of its category-comprising 465,470 entries that cover 97 ADMET endpoints (including 43 regression tasks, the highest number reported to date). By integrating the 97 resultant prediction models with 36 computed physicochemical properties, we have developed and made publicly available a free, high-throughput online ADMET prediction tool with 133 endpoints which is poised to serve as a novel and valuable alternative for guiding early-stage drug discovery and safety assessment.