Molecular dynamics (MD) simulations are widely used to study the mechanisms of biological processes at an atomistic resolution. Most physiological events, e.g., drug-target binding and protein folding, occur at timescales beyond milliseconds. But, we can simulate only up to a few microseconds at an affordable computational cost. Enhanced sampling algorithms can accelerate conformational sampling by applying an 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. Deep neural networks can be used to construct CVs using a generic and system-agnostic feature space to compute an accurate free energy surface for complex molecular processes. However, their lack of interpretability and high cost of evaluation during trajectory propagation make NN-CVs difficult to apply to biomolecular processes. In addition, it often requires a large amount of training data to build NN-CVs that align well with the slow modes of the system, thereby increasing the overall computational cost. In this talk, I will describe a surrogate model approach to express the output of a neural network as a linear combination of a subset of the input descriptors. In addition to providing mechanistic insights due to their explainable nature, the surrogate model CVs exhibit negligible losses in efficiency and accuracy compared to the NN-CVs in reconstructing the underlying free energy surface. Surrogate model CVs are less expensive to evaluate compared to their neural network (NN) counterparts, making them suitable for enhanced sampling simulations of large and complex biomolecular processes. I will also describe a variational Koopman algorithm for data-efficient training of deep learning CVs using short metadynamics trajectories, sampling only forward transitions. I will show some preliminary applications of enhanced sampling methods on RNA conformational dynamics and ligand binding
Dhiman grew up in Kolkata, India, and graduated from Indian Institute of Science Education and Research (IISER) Kolkata with an integrated BS-MS dual degree in Chemistry in 2018. He completed his PhD in Chemistry in 2022 from the University of California Irvine working with Prof. Ioan Andricioaei. Subsequently, he held a postdoctoral position in the group of Prof. Michele Parrinello at the Italian Institute of Technology, Genoa, Italy. Since the fall of 2024 he is an assistant professor at the Department of Chemistry and Biochemistry, at the University of Oregon, USA.