# Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations

arXiv: https://arxiv.org/abs/2108.09637 · HTML: https://mlsanity.com/paper/2108.09637/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 22 Aug 2021.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Geometric deep learning: going beyond Euclidean data](https://mlsanity.com/paper/1611.08097/) (2016)
- [Convolutional Networks on Graphs for Learning Molecular Fingerprints](https://mlsanity.com/paper/1509.09292/) (2015)
- [Graph U-Nets](https://mlsanity.com/paper/1905.05178/) (2019)
- [Molecular Graph Convolutions: Moving Beyond Fingerprints](https://mlsanity.com/paper/1603.00856/) (2016)
- [Optimization of Molecules via Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08678/) (2018)
- [Molecular geometry prediction using a deep generative graph neural network](https://mlsanity.com/paper/1904.00314/) (2019)
- [Mol-CycleGAN - a generative model for molecular optimization](https://mlsanity.com/paper/1902.02119/) (2019)
- [Mean-field theory of graph neural networks in graph partitioning](https://mlsanity.com/paper/1810.11908/) (2018)

## Abstract (arXiv)

Tackling molecular optimization problems using conventional computational methods is challenging, because the determination of the optimized configuration is known to be an NP-hard problem. Recently, there has been increasing interest in applying different deep-learning techniques to benchmark molecular optimization tasks. In this work, we implement a graph-convolutional method to classify molecular structures using the equilibrium and non-equilibrium configurations provided in the QM7-X data set. Atomic forces are encoded in graph vertices and the substantial suppression in the total force magnitude on the atoms in the optimized structure is learned for the graph classification task. We demonstrate the results using two different graph pooling layers and compare their respective performances.