  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.
