Understanding small molecule conformations using statistical machine learning - Chan, LS (2020)
The generation of conformations for small molecules is one of the cornerstones of computational chemistry. Identifying diverse low energy conformers and, in particular, the lowest energy conformer are essential for applications, such as molecular docking and molecular property predictions. The large conformational space of flexible molecules and the high computational cost of accurate energy evaluation with methods such as quantum mechanics are two key challenges.