  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.
