Department of Chemistry and Biochemistry
Physical Chemistry Seminar Series

Professor Dhiman Ray, University of Oregon
October 28th, 2024 —2:00pm
Tykeson 140

Title: Deep Learning Augmented Simulation of Biomolecules

Molecular dynamics (MD) simulations are used extensively to study the mechanisms of biological processes in atomistic resolution. Most physiological events, e.g. drug-target binding and protein folding, occur at beyond millisecond timescales. But, we can simulate only up to a few microseconds at an affordable computational cost. Enhanced sampling algorithms such as umbrella sampling, metadynamics, etc. can accelerate conformational sampling by applying external biasing potential. The accuracy and efficiency of these algorithms are sensitive to the choice of collective variable (CV), a low dimensional space along which the bias is applied. Intuitive CVs, e.g. distances, angles, etc. are often insufficient to adequately sample the conformational landscape.

Machine learning algorithms can play a key role in addressing these challenges. We demonstrated that collective variables constructed using deep neural networks with a generic and system-agnostic feature space provide accurate free energy surface for complex molecular systems e.g. protein folding and ligand binding. Using it in combination with the novel On-the-fly probability enhanced sampling (OPES) flooding algorithm, the kinetic properties can also be recovered. Integrating them with explainable artificial intelligence (XAI) methods such as surrogate models can help interpret mechanisms while further improving the sampling efficiency.

The Ray group works on the development and application of these algorithms to study complex biomolecular processes relevant to drug discovery, antibiotic resistance, and rational design of monoclonal antibodies.