Strain Problems got you in a Twist? Try StrainRelief: A Quantum-Accurate Tool for Ligand Strain Calculations
This provides a more accurate tool for drug discovery teams to filter ligand strain in small molecule design, though it is incremental as it improves upon existing neural network potentials.
The authors tackled the problem of accurately calculating ligand strain energy in drug design by developing StrainRelief, a tool that uses a MACE Neural Network Potential trained on DFT data to achieve quantum accuracy, estimating strain energy differences within 1.4 kcal/mol relative to DFT.
Ligand strain energy, the energy difference between the bound and unbound conformations of a ligand, is an important component of structure-based small molecule drug design. A large majority of observed ligands in protein-small molecule co-crystal structures bind in low-strain conformations, making strain energy a useful filter for structure-based drug design. In this work we present a tool for calculating ligand strain with a high accuracy. StrainRelief uses a MACE Neural Network Potential (NNP), trained on a large database of Density Functional Theory (DFT) calculations to estimate ligand strain of neutral molecules with quantum accuracy. We show that this tool estimates strain energy differences relative to DFT to within 1.4 kcal/mol, more accurately than alternative NNPs. These results highlight the utility of NNPs in drug discovery, and provide a useful tool for drug discovery teams.