Efficient Search for Energetically Favorable Molecular Conformations against Metastable States via Gray-Box Optimization

In order to accurately
understand and estimate molecular properties,
finding energetically favorable molecular conformations is the most
fundamental task for atomistic computational research on molecules
and materials. Geometry optimization based on quantum chemical calculations
has enabled the conformation prediction of arbitrary molecules, including de novo ones. However, it is computationally expensive to
perform geometry optimizations for enormous conformers. In this study,
we introduce the gray-box optimization (GBO) framework, which enables
optimal control over the entire geometry optimization process, among
multiple conformers. Algorithms designed for GBO roughly estimate
energetically preferable conformers during their geometry optimization
iterations. They then preferentially compute promising conformers.
To evaluate the performance of the GBO framework, we applied it to
a test set consisting of seven dipeptides and mycophenolic acid to
determine their stable conformations at the density functional theory
level. We thus preferentially obtained energetically favorable conformations.
Furthermore, the computational costs required to find the most stable
conformation were significantly reduced (approximately 1% on average,
compared to the naive approach for the dipeptides).