Graph-Convolutional Deep Learning to Identify Optimized Molecular Configurations
This work addresses molecular optimization, a challenging NP-hard problem, but is incremental as it applies an existing graph-convolutional approach to a specific dataset.
The paper tackled molecular optimization by implementing a graph-convolutional method to classify molecular structures using the QM7-X dataset, achieving results with two different graph pooling layers and comparing their performances.
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.