# Schr"odinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space

arXiv: https://arxiv.org/abs/1912.05079 · HTML: https://mlsanity.com/paper/1912.05079/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2019.

## Built on by (most cited first, 3 of 3)

- [NNP/MM: Accelerating molecular dynamics simulations with machine learning potentials and molecular mechanic](https://mlsanity.com/paper/2201.08110/) (2022)
- [Broadening the Scope of Neural Network Potentials through Direct Inclusion of Additional Molecular Attributes](https://mlsanity.com/paper/2403.15073/) (2024)
- [Generalizability of Graph Neural Network Force Fields for Predicting Solid-State Properties](https://mlsanity.com/paper/2409.09931/) (2024)

## Abstract (arXiv)

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.