Efficient and Accurate Ligand Strain Calculations in Solution with the AIMNet2 Neural Network Potential

Conformational strain, the energy a ligand pays to adopt its bound conformation, contributes to binding affinity but is routinely misestimated. The standard estimate references a single gas-phase energy minimum. This ignores solvent and mishandles charged ligands: in vacuo their intramolecular electrostatics collapse them into compact minima that deflate the unbound reference and inflate the apparent strain. We introduce AIMNet2-CPCM, a neural network potential trained to B97-3c energies and forces in a conductor-like continuum solvent (CPCM). It treats neutral and charged species on the same footing, and its batched GPU inference makes a solvated global-minimum search practical across an entire screening library. Across ∼8K PDB ligands the solvated strain has a median of 4.0 kcal/mol and rises with net charge. The trend is well determined through a net charge of -2. The -3 and -4 polyanions dominate the high-strain tail, where continuum solvation is least reliable, and their values are reported as upper bounds. Gas-phase force fields reproduce the neutral strains but overstate the charged ones, because they collapse the unbound minimum that solvent screening keeps open. Adding a continuum solvent to the force field removes most of this error and confirms the mechanism. Profiling at this scale further shows that the same ligand strains very differently across the targets that bind it: ligand strain is a property of the bound pose, not a transferable molecular descriptor.