Deva is an Associate Professor and Head of the Center for Computational Natural Sciences and Bioinformatics at International Institute of Information Technology Hyderabad. He has been in IIIT Hyderabad for the past ten and have been working in the areas of protein folding, ion channels/transporters, heterogenous nucleic acids and metal nanoparticles. Recently, the research group has started using modern machine learning algorithms for applications in chemistry and biology. Deva has obtained awards such as the INSA young scientist medal, DBT-IYBA award and AICTE young teacher award, and more recently the JSPS invitation fellowship and distinguished lectureship award by the Chemical Society of Japan.
Abstract OpenTox Asia 2019
Rise of the Machines: Chemistry with Machine Learning
Recent advances in deep learning methods seemed to have resulted in resurgence of their applications in natural sciences during the last few years. Fundamentally, these data driven methods can broadly be classified as supervised and unsupervised methods. In the first part of the presentation, we will discuss the use of artificial neural network for predicting energies of small molecules. The ANN model was obtained based on a novel molecule featurization inspired by additive force fields (BAND: bag of Bonds, Angles, Nonbonds and Dihedrals). We will show that this model is applicable not only to the class of molecules that were used for the training, but also to more complex molecules. While there is certainly room for improvement, the apparent potential energy function can also be used to perform geometry optimization. In the second part of the talk, we will present the use of unsupervised machine learning along with graph theory to extract folding pathways from replica exchange molecular trajectories. A suitable vector representation was chosen for each frame in the macromolecular trajectory and dimensionality reduction was performed using PCA. The trajectory was then clustered using a density-based clustering algorithm, where each cluster represents a meta-stable state on the energy surface of the biomolecule. A graph was created with these clusters as nodes. We hypothesize that the most probable path of (un)folding from a starting to an ending state is the widest path (path which has maximum minimum edge weight) along the graph. Our method makes the understanding of the mechanism of unfolding in RNA hairpin molecule more tractable. As this method doesn’t rely on temporal data it can be used to analyse trajectories from Monte Carlo sampling techniques and replica exchange molecular dynamics (REMD).