While generating ensembles of molecular conformers are crucial for drug discovery and a wide range of computational chemistry tasks, the typical solution is to create a geometrically diverse group. We have used Bayesian optimization algorithms including a data-driven analysis of correlated dihedral angles to look for low energy conformers. The method performs dramatically better at finding low-energy structures with a fixed search budget than random search or similar genetic algorithm methods both with classical force fields and DFTB methods. Using open crystallographic databases and exhaustive conformer sampling using DFTB, we find a range of correlated dihedrals, ranging from steric clashes to hydrogen bonding and π-π stacking motifs. The efforts also give estimates of molecular entropy, including from thermal occupation of multiple conformers. Using both standard QSAR approaches and neural network machine learning, we can provide accurate estimates of quantum chemical entropies.

Efficient Bayesian sampling of molecular conformers: Understanding torsional correlations and entropy effects

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

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