Pioneering the intersection of Agentic AI, machine learning, and structural biology to accelerate target identification and drug discovery focusing on undruggable targets. Based in India, building pipelines and tools trusted by researchers worldwide.
Building autonomous AI agents for drug discovery workflows — from target identification to lead optimisation — leveraging LLMs, PyTorch, and TensorFlow for end-to-end automation.
Applying computational genomics, structural bioinformatics, and ML to identify and validate novel drug targets — including cryptic pockets, undruggable targets, and aging-related biology.
Expert in molecular docking, pharmacophore modelling, and gigascale virtual screening against vendor libraries (Enamine REAL, LCC) using cloud HPC infrastructure.
Deep expertise in NAMD, GROMACS, AMBER, and OpenMM. Open-source MD Notebooks repository with 42 stars used by researchers globally.
Developing QSAR/QSPR models, cheminformatics pipelines, and ML-SAR workflows using KNIME and Jupyter. 1 US patent and extensive published models.
One of India's first researchers to computationally study aging and longevity targets. Leading work on combinatorial therapies for age-linked dysfunctions and Indian population studies.
Dr. Girinath G. Pillai is an Expert Computational Chemist, where he drives computational drug discovery projects spanning giga-scale virtual screening, generative AI chemistry, Molecular Glues, PROTAC modelling, and SAR optimisation.
He completed his PhD jointly at the University of Florida (with Prof. Katritzky) and the University of Tartu, Estonia (with Prof. Karelson), and subsequently held a Marie Curie Research Fellowship at Molcode Ltd., Estonia.
A recognised researcher, educator and mentor, Dr. Pillai has delivered 2000+ training sessions globally, coordinates India's Drug Discovery Hackathon, and mentors through NITI Aayog's Atal Tinkering Labs. His open MD Jupyter Notebooks, Orange Workflows and KNIME resources are widely used by the global research community.
Agentic AI, gigascale VS, PROTAC modelling, Generative AI chemistry
National lead for computational drug discovery mentorship
Drug discovery Software startup; ML-SAR and medicinal chemistry
EU FP7 programme; molecular modelling & QSAR
Prof. Mati Karelson's group; computational chemistry
Prof. Alan R. Katritzky's group; QSAR & medicinal chem
Jupyter Notebooks for Molecular Dynamics — GROMACS, AMBER, NAMD, OpenMM and docking workflows in a single reproducible environment.
Running Modeller for protein homology modelling using Python and Jupyter Notebooks. Beginner-friendly entry point to structural biology.
QSAR/QSPR management tools and workflows for building quantitative structure-activity relationship models using ML pipelines.
Getting started with Jupyter, Python, scientific libraries, GitHub and computational models — a guide for life science researchers.
Datasets and models for designing novel molecules targeting aging-related biological pathways — longevity enrichment and inhibition.
Free KNIME workflows for cheminformatics, drug discovery data analysis and machine learning pipelines, contributed to community.