# Molecular modeling with machine-learned universal potential functions

arXiv: https://arxiv.org/abs/2103.04162 · HTML: https://mlsanity.com/paper/2103.04162/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 6 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Molecular Graph Convolutions: Moving Beyond Fingerprints](https://mlsanity.com/paper/1603.00856/) (2016)
- [A Two-Step Graph Convolutional Decoder for Molecule Generation](https://mlsanity.com/paper/1906.03412/) (2019)
- [Chemi-net: a graph convolutional network for accurate drug property prediction](https://mlsanity.com/paper/1803.06236/) (2018)
- [Prediction of amino acid side chain conformation using a deep neural network](https://mlsanity.com/paper/1707.08381/) (2017)

## Abstract (arXiv)

Molecular modeling is an important topic in drug discovery. Decades of research have led to the development of high quality scalable molecular force fields. In this paper, we show that neural networks can be used to train a universal approximator for energy potential functions. By incorporating a fully automated training process we have been able to train smooth, differentiable, and predictive potential functions on large-scale crystal structures. A variety of tests have also been performed to show the superiority and versatility of the machine-learned model.