Dr. Subhendu Roy

Research

Research   Understanding and Engineering Biology through AI, Physics and Molecular Simulations   Proteins are Nature's molecular machines. Our laboratory seeks to understand how they recognize, move, evolve and catalyze chemical reactions at atomic resolution. By integrating Artificial Intelligence (AI), molecular simulations, statistical physics and quantum chemistry, we aim to uncover the physical principles governing biomolecular function and translate them into next-generation strategies for enzyme engineering, drug discovery and sustainable biotechnology. Our vision is to build predictive computational models that not only explain biological phenomena but also guide the rational design of proteins with new functions. We combine physics-based simulations with AI-driven approaches to bridge sequence, structure, dynamics and function.   Our Research Vision   Our research lies at the interface of Biology, Chemistry, Physics and Artificial Intelligence. We investigate how conformational dynamics, molecular interactions and electronic structure give rise to biological function, and how these principles can be harnessed to design new biomolecules.   Research Themes   AI-Guided Enzyme Design   Combining AI, molecular simulations and QM/MM methods to understand catalytic mechanisms and design enzymes for pharmaceuticals, green chemistry, biofuels and plastic biodegradation.   Computational Drug Discovery   Structure-based drug design for GPCRs, membrane proteins, kinases and other therapeutic targets using enhanced sampling, free-energy calculations and AI-assisted modeling.   Energy Conversion and Membrane Biology   Proton, electron and ion transport in photosystems, respiratory complexes, rhodopsins and ion channels.   Protein Dynamics and Molecular Machines   Mechanisms of allostery, folding landscapes, intrinsically disordered proteins, peptide self-assembly and large conformational transitions.   Method Development   Development and application of multiscale molecular dynamics, enhanced sampling, coarse-grained models and hybrid QM/MM simulations on modern GPU/HPC platforms.   Our Approach   •           Artificial Intelligence and machine learning •           Atomistic molecular dynamics simulations •           Enhanced sampling and free-energy calculations •           Hybrid QM/MM simulations of enzyme catalysis •           Coarse-grained simulations of large biomolecular systems •           High-performance GPU computing •           Close interaction with experimental collaborators for validation   Why Join Our Group?   We are building an interdisciplinary computational biology laboratory where students are encouraged to tackle ambitious scientific problems with intellectual independence. Members receive rigorous training in modern computational biology, scientific programming, AI-enabled molecular modeling, advanced simulation methodologies and quantitative biophysics. Our goal is to develop researchers who can contribute to academia, biotechnology and the pharmaceutical industry.   Open Positions   We welcome motivated PhD students and postdoctoral researchers with backgrounds in Biology, Physics, Chemistry, Biochemistry, Biotechnology, Bioinformatics, Mathematics, Computer Science or related disciplines. Curiosity, enthusiasm and a willingness to learn are valued more than prior experience in computational biology.