TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search
This addresses the challenge of efficient conformer search for medium to large molecules in computational chemistry, with applications in biomolecules and materials like renewable energy, representing a strong specific gain rather than a broad paradigm shift.
The paper tackled the problem of molecular geometry prediction for flexible molecules by introducing TorsionNet, a reinforcement learning-based sequential conformer search technique, which outperformed the highest scoring chemoinformatics method by 4x on large branched alkanes and by several orders of magnitude on biopolymer lignin.
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.