AI identifies inhibitor for 'undruggable' protein linked to pancreatic cancer

Drug discovery has traditionally been a slow, expensive, and largely trial-and-error process. Identifying a molecule that binds to a specific protein target, blocks its harmful activity, and is safe for human use can take over a decade and cost billions of dollars. A central challenge is that many proteins involved in cancer are classified as 'undruggable', their surface geometry or signalling complexity makes it nearly impossible for conventional small molecules to bind them effectively.

Computational approaches to drug discovery have existed since the 1980s, but their power was limited by processing speed and the quality of predictive models. The rise of machine learning and generative AI has transformed this landscape. AI systems can now screen tens of thousands of molecular candidates, predict how they will interact with a target protein, assess toxicity and absorption properties, and narrow the field to a handful of viable candidates, all in a fraction of the time traditional methods require. This convergence of biology, chemistry, and AI is sometimes called computational drug discovery or AI-driven drug design.

India has a growing stake in this space. The country has a strong generic pharmaceutical industry, a large pool of bioinformatics and AI talent, and a significant disease burden from cancers that lack affordable treatments. Collaborations between Indian AI startups and global research institutions represent an emerging model for translating computational capability into clinical impact.