IIT Madras Launches AI Framework To Aid Discovery Of Next-Gen Drugs

The Indian Institute of Technology-Madras (IITM) announced on Monday a breakthrough artificial intelligence framework that can rapidly generate drug-like molecules that are easier to synthesise in real-world laboratory settings.

Researchers from IITM's Robert Bosch Centre for Data Science and AI, Wadhwani School of Data Science and AI (WSAI) collaborated with researchers from Ohio State University in the United States to develop the framework.

According to a press release issued by IITM, the framework promises to significantly cut down the early-stage timelines of drug development -- currently a billion-dollar, decade-long process -- and could play a crucial role in addressing drug resistance in cancer and infectious diseases.

The new framework, called ‘PURE' (Policy-guided Unbiased REpresentations for Structure-Constrained Molecular Generation (SCMG), stands apart from existing molecule-generation AI tools that rely on rigid scoring mechanisms or statistical optimisation, stated the press release.

Prof B Ravindran, Head, WSAI, said, “What's unique about PURE is the way it uses reinforcement learning, not just to optimise specific metrics, but to learn how molecules transform. By treating chemical design as a sequence of actions guided by real reaction rules, PURE moves us closer to AI systems that can reason through synthesis steps much like a chemist would.” The press release also stated that PURE was evaluated on widely accepted molecule-generation benchmarks, including QED (drug-likeness), DRD2 (dopamine receptor activity), and solubility tests.

Prof Karthik Raman, WSAI, IIT Madras, said, “PURE adopts a novel approach to mapping chemical space, without being biased towards a specific metric -- a common failing of existing tools. Further, it grounds the search of the vast chemical space for novel molecules in synthesisability, by generating molecules that are likely to be synthesisable in the lab, through a novel reaction rule-based approach.” According to Prof Srinivasan Parthasarathy, Department of Computer Science and Engineering, Ohio State University, PURE offers game-changing early-stage discovery benefits for pharmaceutical research, with the capability to identify alternative (more effective) drug candidates in the face of resistance and hepatotoxicity.

"It blends cutting-edge self-supervised learning with policy-based reinforcement learning, using template-driven molecular simulations to navigate the discrete molecular search space while mitigating metric leakage. In addition to drug discovery, the PURE framework provides a promising foundation for accelerating the discovery of new materials, an important future research direction,” he added.

The findings were published in the reputed, peer-reviewed Journal of Cheminformatics, an open-access research on how computational methods, data science, and machine learning are used to analyse and design chemical systems, added the researchers.  

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