Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins

doi:10.1039/D6DD00056H

Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins

2026-06-09

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DOI：10.1039/D6DD00056H

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Machine learning interatomic potentials (MLIPs) enable efficient modeling of molecular interactions with quantum mechanical (QM) accuracy for complex systems. However; constructing robust and representative training datasets that capture subtle; system-specific interaction motifs remains challenging. Here; a PAirwise Non-covalent Interaction Potential (PANIP) is introduced; an ensemble MLIP model built upon the Neural Equivariant Interatomic Potentials (NequIP) framework and trained on non-covalent interactions (NCIs) between protein-derived fragments. PANIP is trained using an automated multi-fidelity active learning (MFAL) workflow that distills a diverse and information-rich subset from an otherwise prohibitively large pool of fragment dimers extracted from high-resolution protein structures in the Protein Data Bank (PDB). This strategy concentrates high-level QM calculations (ωB97X-D3BJ/def2-TZVPP) on the most informative structures while preserving comprehensive coverage of the data distribution. Applied to dimers constructed from 17 chemically distinct protein fragments (side chains; backbone motif; and water); this workflow yields the PDB Fragment Interaction Dataset (PDB-FRAGID); a condensed yet representative subset comprising only 8.7% of the original 36.3 million-dimer pool while maintaining structural and chemical diversity. Despite this drastic reduction; PANIP retains ωB97X D3BJ/def2 TZVPP-level accuracy on both equilibrium and non‑equilibrium fragment configurations and achieves mean absolute errors below 0.2 kcal/mol on out‑of‑distribution systems; demonstrating excellent transferability across diverse NCI motifs. Compared to the widely used ANI 2x potential; PANIP delivers substantially lower errors on protein‑derived fragments and exhibits superior generalization; particularly for charged and strongly interacting dimers. By combining a fragmentation-based energy decomposition scheme with PANIP; protein–ligand binding energies can be estimated at near force‑field computational cost while preserving QM‑level accuracy for pairwise NCIs; enabling its use as a fragment‑based scoring function that rivals specialized docking scoring functions despite being trained solely on fragment dimers. The PANIP models and associated benchmark sets are available at https://github.com/hnlab/PANIP; and the PDB‑FRAGID dataset is available at https://github.com/hnlab/PDB‑FRAGID.

Developing a Machine-Learning Interatomic Potential for Non-Covalent Interactions in Proteins

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