My thesis research focuses on developing advanced many-body polarizable force fields (MB-UCB) for biomolecules using energy decomposition analysis (EDA), as well as studying the bio-molecular interactions. I’m also involved in a drug-discovery project where we use deep reinforcement learning to generate viral inhibitors and apply physics-based methods including docking, molecular dynamics and free energy perturbation to evaluate the potency.

I am dedicated to investigating the intricate dynamics of protein flexibility within the realm of drug discovery. My research focuses on utilizing computational methodologies, including both classical approaches and deep learning techniques, to evaluate the entropy associated with protein-ligand binding interactions.

My research aims to harness the full potential of machine learning models. By leveraging the accuracy and efficiency of these models, we can achieve precise predictions of high-order derivatives without the need for explicit training on them. This capability extends to calculating Hessians on potential energy surfaces, which are essential for optimizing transition states, as well as predicting chemical shifts with ab initio quality and force field efficiency.

My research focuses on the development of reactive force fields informed by molecular properties of reactivity, such as the Mayer and Wiberg bond indices. I use a combination of machine learning and empirical models to enable future large-scale and routine studies of reactive systems.

My current research focuses on developing methodology for antiviral discovery. To do this, I'm turning large language models (LLMs) into chemical language models (CLMs) with the ability to generate drug-like molecules with specific properties. I'm also optimizing various compounds for antiviral ability.

My research focuses on developing accurate free energy methods in drug discovery. To this end, I build machine learning models that integrate evolutionary sequence conservation and protein structure information to further our understanding of protein-ligand interactions.

My research focuses on developing advanced multipolar polarizable force fields (CMM) that aim to reproduce quantum mechanical potential energy surfaces with greater fidelity than traditional molecular mechanics models. I also work on building tools for generating molecule-specific force fields, with the ultimate goal of applying these methods to achieve a more accurate description of condensed phase systems.

My research focuses on developing novel machine learning models that directly predict Hessian-related properties, bypassing the computationally demanding derivatives of traditional methods. This approach yields substantial improvements in time and memory efficiency. A key application is the calculation of the Hessians' leftmost eigenvectors, which are essential for transition state optimization, thereby enabling the study of reactivity in complex, large-scale molecular systems.