DeepDOX1: A Dual-Drive Framework Integrating Deep Learning and First-Principles Physics for Drug-Protein Affinity Prediction
Rédigé par Zheng Liu , Hao Sun , Yuliang Wang , Yanliang Ren , Li Rao , Zeyue Huang , Hongxuan Cao , Xiuqi Hu , Xinyue Zhu , Meng Li et Jian Wan
Publié 15 décembre 2025
Serveur de preprints bioRxiv
Résumé
In this work, we present DeepDOX1, a dual-drive drug-protein affinity (DPA) prediction tool features the tight integration of a concise AI architecture and a quantum mechanics based representation. The first-principle physics generated features incorporating the interactions between the drug and the protein pocket allows a relatively simple CNN model trained on a relatively small training set to exhibit exceptional generalization capabilities across extensive testing. Notably, DeepDOX1 outperforms popular AI models in the tests simulating real-world drug design scenario and a highly challenging test set featuring covalent ligands, halogenated ligands and metalloproteins. In addition, we designed a series of novel covalent inhibitors targeting the diabetes target hu -FBPase using DeepDOX1. Subsequent experimental validation including enzyme-level bioactivity assays and crystal structure determination confirmed the DeepDOX1’s effectiveness in real-world drug design applications. It is conceivable that the combination of AI and first-principle QM might be one of the next breakthrough points of DPA prediction.
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