Agentic AI, biology foundation models, and GPU-native chemistry are converging to rewrite drug discovery. The Giri Pillai Lab works at that frontier — from autonomous co-scientists to longevity therapeutics.
Autonomous LLM agents (GPT-class, Claude, open Llama/Mistral) that plan experiments, call docking/MD tools, critique their own hypotheses, and iterate on molecular design with minimal human steering. The lab partner of 2026–2036.
AlphaFold 3, Boltz, RoseTTAFold All-Atom, ESM, and Chai-1 reshaping structural biology. Fine-tuned protein & molecule foundation models are becoming the new substrate for SAR, ADMET, and target discovery.
Diffusion models, flow-matching, and equivariant generative networks design novel scaffolds with built-in synthetic accessibility — pushing de novo design from curiosity to production tool over the next decade.
Hallmarks-of-ageing targets, senolytics, partial reprogramming, and AI-driven biomarker discovery. By 2036, computational longevity moves from research curiosity to credentialed therapeutic modality.
GPU-accelerated MD (OpenMM, GROMACS-CUDA), neural network potentials (MACE, NequIP), and early quantum-assisted free-energy methods — compressing physics-grade simulations from weeks to hours.
KNIME, Jupyter, and modern MLOps stacks (W&B, MLflow, Hugging Face) keeping discovery transparent and reproducible — open datasets for SARS-CoV-2, tyrosinase inhibitors, and longevity targets.
A pragmatic mix of open-source cheminformatics, proprietary modelling suites, and modern ML — chosen per problem.