DeepDOX1: A Dual-Drive Framework Integrating Deep Learning and First-Principles Physics for Drug-Protein Affinity Prediction

Liu, Z.; Sun, H.; Wang, Y.; Ren, Y.; Rao, L.; Huang, Z.; Cao, H.; Hu, X.; Zhu, X.; Li, M.; Wan, J.

doi:10.64898/2025.12.12.693818

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bioRxiv · 10.64898/2025.12.12.693818

DeepDOX1: A Dual-Drive Framework Integrating Deep Learning and First-Principles Physics for Drug-Protein Affinity Prediction

Liu, Z.·Sun, H.·Wang, Y.·Ren, Y.·Rao, L.·Huang, Z.·Cao, H.·Hu, X.·Zhu, X.·Li, M.·Wan, J.

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Abstract

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 DeepDOX1s 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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Liu, Z., Sun, H., Wang, Y., Ren, Y., Rao, L., Huang, Z., Cao, H., Hu, X., Zhu, X., Li, M., Wan, J.. 2025-12-15. DeepDOX1: A Dual-Drive Framework Integrating Deep Learning and First-Principles Physics for Drug-Protein Affinity Prediction. https://doi.org/10.64898/2025.12.12.693818

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