Accurate and Rapid Ranking of Protein–Ligand Binding Affinities Using Density Matrix Fragmentation and Physics‐Informed Machine Learning Dispersion Potentials

The generalized many‐body expansion for building density matrices (GMBE‐DM), truncated at the one‐body level and combined with a purification scheme, is applied to rank protein–ligand binding affinities across two cyclin‐dependent kinase 2 (CDK2) datasets and one Janus kinase 1 (JAK1) dataset, totaling 28 ligands. This quantum fragmentation‐based method achieves strong correlation with experimental binding free energies ( R 2 = 0.84), while requiring less than 5 min per complex without extensive parallelization, making it highly efficient for rapid drug screening and lead prioritization. In addition, our physics‐informed, machine learning‐corrected dispersion potential (D3‐ML) demonstrates even stronger ranking performance ( R 2 = 0.87), effectively capturing binding trends through favorable cancelation of non‐dispersion, solvation, and entropic contributions, emphasizing the central role of dispersion interactions in protein–ligand binding. With sub‐second runtime per complex, D3‐ML offers exceptional speed and accuracy, making it ideally suited for high‐throughput virtual screening. By comparison, the deep learning model Sfcnn shows lower transferability across datasets ( R 2 = 0.57), highlighting the limitations of broadly trained neural networks in chemically diverse systems. Together, these results establish GMBE‐DM and D3‐ML as robust and scalable tools for protein–ligand affinity ranking, with D3‐ML emerging as a particularly promising candidate for large‐scale applications in drug discovery.