From Targets to Hits: The Emerging AI Ecosystem in Drug Discovery with Aqib Hasnain, Mithrl and Cheng Hu, Technetium Therapeutics

Perhaps more than in any other field, AI is impacting drug discovery and development. To begin the year we’re joined by two AI software-as-service companies, one on the target discovery side and the other built for new compound identification for those targets.

Theral speaks with Aqib Hasnain, Product Lead at Mithrl, and Cheng Hu, co-founder and CEO of Technetium Therapeutics, about how scientists can go from AI generated insights to AI generated assets, from AI-driven fast science, to AI-driven fast drug discovery.

Aqib describes Mithrl as a virtual lab partner focused on shrinking the time between experiments by letting scientists interrogate their own data directly. One of the biggest lessons in building Mithrl, he says, was how much transparency matters. Biologists need to understand the methodology through and through, and this translates directly to how Mithrl works.

“Scientists need to be able to scrutinize and trace everything—because it’s their responsibility to make the next decision.”

Cheng explains Technetium’s vision of an “AI-driven hatchery of novel medicines,” using design-based, physics-guided approaches to move from target discovery to small-molecule hits in weeks rather than years as has been the case screening libraries of millions of compounds. Reflecting on the promise of AI co-scientists, he points to the industry’s biggest unmet need.

“There’s a very serious deficit of novel therapeutic targets and also a very serious deficit of novel chemicals.”

Together, the conversation explores how these two AI tools for target discovery and hit generation are beginning to reshape drug discovery workflows—and how a new ecosystem of services is developing that is redefining the field.

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