# Graph Nets for Partial Charge Prediction

arXiv: https://arxiv.org/abs/1909.07903 · HTML: https://mlsanity.com/paper/1909.07903/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 5 of 5)

- [Graph Neural Networks: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2108.10733/) (2021)
- [EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment](https://mlsanity.com/paper/2302.06758/) (2023)
- [On the design space between molecular mechanics and machine learning force fields](https://mlsanity.com/paper/2409.01931/) (2024)
- [Advancements in Molecular Property Prediction: A Survey of Single and Multimodal Approaches](https://mlsanity.com/paper/2408.09461/) (2024)
- [Efficient Graph Coloring with Neural Networks: A Physics-Inspired Approach for Large Graphs](https://mlsanity.com/paper/2408.01503/) (2024)

## Abstract (arXiv)

Atomic partial charges are crucial parameters for Molecular Dynamics (MD) simulations, molecular mechanics calculations, and virtual screening, as they determine the electrostatic contributions to interaction energies. Current methods for calculating partial charges, however, are either slow and scale poorly with molecular size (quantum chemical methods) or unreliable (empirical methods). Here, we present a new charge derivation method based on Graph Nets---a set of update and aggregate functions that operate on molecular topologies and propagate information thereon---that could approximate charges derived from Density Functional Theory (DFT) calculations with high accuracy and an over 500-fold speed up.