A solvated neural-network potential makes ligand strain estimates usable at library scale
- Conventional strain estimates reference a single gas-phase minimum, which ignores solvent and distorts charged ligands whose internal electrostatics collapse them into compact structures in vacuo.
- AIMNet2-CPCM, a neural network potential trained on DFT-level energies and forces in a continuum solvent, handles neutral and charged species on equal footing.
- GPU batch inference makes a solvated global-minimum search feasible across a full screening library.
- Across roughly 8,000 PDB ligands, solvated strain rises with net charge, with a trend that holds up to a charge of −2.
💡 Points
Strain filters are only as good as the unbound reference, and anyone who has seen a zwitterion fold into a salt bridge in vacuo knows why gas-phase numbers mislead. A solvated, charge-aware potential fast enough for library triage is something I would actually put in a docking post-filter. The caveat is the less reliable behavior at higher negative charge, which matters for acids and phosphates.