Schrödinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space
This work addresses the need for accurate and fast potential energy functions in drug discovery, particularly for protein-ligand binding free energy calculations, though it is incremental as it builds on existing methods by expanding element support.
The researchers tackled the limited chemical coverage of neural network potentials for drug discovery by extending support from 41% to 94% of druglike molecules, achieving an RMSE of 0.70 kcal/mol for relative conformer energies, which is substantially better than previous state-of-the-art methods.
We have developed a neural network potential energy function for use in drug discovery, with chemical element support extended from 41% to 94% of druglike molecules based on ChEMBL. We expand on the work of Smith et al., with their highly accurate network for the elements H, C, N, O, creating a network for H, C, N, O, S, F, Cl, P. We focus particularly on the calculation of relative conformer energies, for which we show that our new potential energy function has an RMSE of 0.70 kcal/mol for prospective druglike molecule conformers, substantially better than the previous state of the art. The speed and accuracy of this model could greatly accelerate the parameterization of protein-ligand binding free energy calculations for novel druglike molecules.