Demis Hassabis, chief of DeepMind spinout Isomorphic Labs, believes the company would halve drug discovery times after inking deals with Eli Lilly and Novartis
Chief of Google's AI unit and Isomorphic Labs spells out targets following $3bn Eli Lilly-Novartis partnerships X: @kymawhite . Forums: Slashdot X: Kimberly White / @kymawhite : Thanks to the team at the @FT for covering the latest developments @IsomorphicLabs and our new collaborations w/@EliLillyandCo and @Novartis. https://www.ft.com/... Forums: Msmash / Slashdot : DeepMind Spin-off Aims To Halve Drug Discovery Times Following Big Pharma Deals
Context & Ripple Effects
Isomorphic Labs was created by Alphabet as a dedicated AI drug-discovery company led by Demis Hassabis. Its new work with Eli Lilly and Novartis turns that earlier structure into a commercial test, following the partners’ AI drug-discovery agreements.
The reported goal gives the collaborations an operational benchmark: not simply using AI in research, but shortening the path to candidate medicines. It also connects DeepMind-derived biology research to a standalone company designed to commercialize it.
First-order effects
- Isomorphic Labs now has a public speed target against which Eli Lilly and Novartis can assess the value of the collaborations; the target remains an ambition rather than a reported outcome.
- Eli Lilly and Novartis gain access to Isomorphic’s AI-driven discovery approach under the partnerships, while Isomorphic gains pharmaceutical partners to apply and validate its platform.
Second-order effects
- The collaborations make pharmaceutical-company access and real-world validation more important competitive assets for AI drug-discovery vendors, beyond model development alone.
- Performance-linked economics in the Lilly and Novartis partnerships align Isomorphic’s commercial upside with downstream research progress, increasing pressure to demonstrate tangible gains rather than technical promise.
Third-order effects
- If AI platforms repeatedly reduce discovery timelines in partner programs, drug discovery could shift toward a model where AI specialists supply core research infrastructure while pharmaceutical companies contribute development expertise and commercialization capacity.
- The key constraint will be whether faster discovery translates into successful development outcomes; speed claims alone will not establish a durable change in pharmaceutical R&D economics.
The trend: AI labs are seeking legitimacy and revenue in life sciences by pairing frontier biology capabilities with established pharmaceutical companies’ development pipelines.