Molecular geometry prediction of flexible molecules, or conformer search, is a long-standing challenge in computational chemistry. This task is of great importance for predicting structure-activity relationships for a wide variety of substances ranging from biomolecules to ubiquitous materials. Substantial computational resources are invested in Monte Carlo and Molecular Dynamics methods to generate diverse and representative conformer sets for medium to large molecules, which are yet intractable to chemoinformatic conformer search methods. We present TorsionNet, an efficient sequential conformer search technique based on reinforcement learning under the rigid rotor approximation. The model is trained via curriculum learning, whose theoretical benefit is explored in detail, to maximize a novel metric grounded in thermodynamics called the Gibbs Score. Our experimental results show that TorsionNet outperforms the highest scoring chemoinformatics method by 4x on large branched alkanes, and by several orders of magnitude on the previously unexplored biopolymer lignin, with applications in renewable energy.
@article{arxiv.2006.07078,
title = {TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search},
author = {Tarun Gogineni and Ziping Xu and Exequiel Punzalan and Runxuan Jiang and Joshua Kammeraad and Ambuj Tewari and Paul Zimmerman},
journal= {arXiv preprint arXiv:2006.07078},
year = {2020}
}