Quantum mechanical methods for in silico drug design: Force predictions via machine learning with applications to molecular dynamics simulations

This project aims to develop high accuracy computational tools for the prediction of ligand conformations, combining quantum mechanics and machine learning methods. The ability to accurately predict ligand conformation is a key limitation in computer-aided drug design approaches. We will leverage machine learning methods for their ability to capture, at a fraction of the computational cost, the high level quantum chemical calculation of subtle physics governing diverse chemical shape. These methods will be applied using state-of-the-art neural networks and supercomputing, with validation against experimental data sets of druglike molecules.