Weaknesses: Because the idea is new and very interesting, a number of topics can up that could/should be addressed. Is there a way to be certain that the gradient descent using MMFF has the molecule stay on the same basin of the PES that the rigid rotor sampled? It is likely, particularly in crowded conformations that the structure and energy that MMFF reports are not for the same internal angles as the initial torsion angles would suggest. The Gibbs Score is introduced as some completely new idea, but it's essentially related to a (relative) population according to Maxwell Boltzmann statistics. Furthermore, the log of Gibbs score is then a relative free energy, a very intuitive connection with the underlying physics.

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.