AI in Drug Discovery Series: Terray Therapeutics

Research evidence excerpt

AI in Drug Discovery Series: Terray Therapeutics

Idea

July 26, 2026 11:01 PM GMT

Morgan Stanley & Co. LLCMBiopharma | North America Sean Laaman, Ph.D.

Equity Analyst

AI in Drug Discovery Series: Sean.Laaman@morganstanley.comNatasha Arya +1 212 761-4947

Research Associate

Natasha.Arya@morganstanley.com +1 212 761-6294

Terray Therapeutics Katherine Sun

Katherine.Sun@morganstanley.com +1 212 761-5968

We hosted Terray to discuss the company’s AI thesis: better Michael H Riad, Ph.D.

drugs come from better data, not bigger models. By combining ResearchMichael.Riad@morganstanley.comAssociate +1 212 761-1309

proprietary chemistry datasets, automated experimentation, and

AI in a closed loop, Terray aims to drug previously inaccessible

targets, improve POS, and create true first-in-class medicines.

Changing the probability of success is more important than simply accelerating

timelines: A major takeaway from the discussion was Terray's view that the true

transformative opportunity in AI-enabled drug development is not merely

shortening discovery timelines but fundamentally improving the probability of

technical and clinical success. Management believes traditional drug discovery

suffers from low success rates because researchers are often limited to known

chemical scaffolds and crowded targets. By generating proprietary chemistry

datasets and exploring previously inaccessible regions of chemical space, Terray

hopes to identify novel molecular starting points for difficult and “undruggable”

targets. The company repeatedly highlighted that reducing failure rates across the

drug-development value chain is potentially far more valuable than simply making

discovery faster. Their approach is designed to improve hit rates, program