About Myself

303, Block 2,

Department of Biotechnology,

IIT Madras, Chennai - 600036

I am a budding researcher pursuing my PhD at the Indian Institute of Technology Madras (IIT Madras). My academic journey is fueled by a deep fascination for untangling the intricate mysteries of biological systems using the latest technological innovations.

I hold a BTech in Biotechnology from NIT Calicut and am thrilled to contribute to the academic community as a member of the BioSystems Engineering and Control group at IIT Madras. My research primarily delves into the exciting realms of multi-modal artificial intelligence, reinforcement learning, and deep learning, all focusing on their applications within biological modalities. My work has the potential to advance scientific understanding and impact vital areas like industrial biotechnology and drug development.

Beyond my research pursuits, I am committed to sharing knowledge and nurturing the future generation of AI and biology enthusiasts. With an insatiable curiosity, I am dedicated to making meaningful contributions at the exciting crossroads of AI and biology.

News

Oct 26, 2023	I have been selected for the prestigious Prime Minister’s Research Fellowship (PMRF)
Sep 9, 2023	I will be giving a talk in BIOMERS 2023 (Research Scholar Symposium). Excited to present our work in the intersection of natural language processing and biology
Jul 15, 2023	My website is up and running
Jun 15, 2023	Our paper in the Third Conference on Deployable AI won the best paper award
Jun 12, 2023	My presentation in the Annual RBCDSAI Research Showcase won the best poster award

Latest Posts

Nov 16, 2020	Protein-Ligand Binding Site Prediction

Selected Publications

2023

Chemically Interpretable Molecular Representation for Property Prediction

Roshan M S B, and Nirav Pravinbhai Bhatt

In Third Conference on Deployable AI, 2023

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Molecular property prediction using a molecule’s structure is a crucial step in drug and novel material discovery, as computational screening approaches rely on predicted properties to refine the existing design of molecules. Although the problem has existed for decades, it has recently gained attention due to the advent of big data and deep learning. On average, one FDA drug is approved for 250 compounds entering the preclinical research stage, requiring screening of chemical libraries containing more than 20000 compounds. In-silico property prediction approaches using learnable representations increase the pace of development and reduce the cost of discovery. We propose developing molecule representations using functional groups in chemistry to address the problem of deciphering the relationship between a molecule’s structure and property. Functional groups are substructures in a molecule with distinctive chemical properties that influence its chemical characteristics. These substructures are found by (i) curating functional groups annotated by chemists and (ii) mining a large corpus of molecules to extract frequent substructures using a pattern-mining algorithm. We show that the Functional Group Representation (FGR) framework beats state-of-the-art models on several benchmark datasets while ensuring explainability between the predicted property and molecular structure to experimentalists.