Isomorphic Labs Announces Collaborations with Eli Lilly and Novartis for AI-Driven Drug Discovery

by Andrii Buvailo, PhD          News

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Isomorphic Labs, a digital biology company specializing in AI applications, has entered into two distinct research collaborations with Eli Lilly and Company and Novartis. These partnerships focus on employing artificial intelligence for the discovery and development of small-molecule therapeutics, a key area in modern pharmacology.

Under the collaboration with Eli Lilly, Isomorphic Labs is set to receive an initial $45 million with the potential for $1.7 billion in milestone-based payments. This collaboration involves the discovery of therapeutics targeting multiple disease-related proteins and pathways. Similarly, the Novartis partnership, bolstered by an upfront $37.5 million payment, could yield up to $1.2 billion in milestone payments for Isomorphic Labs. This collaboration will focus on three undisclosed targets, which are likely complex biological entities demanding advanced research.

Demis Hassabis, the CEO and founder of Isomorphic Labs, highlighted the use of their proprietary technology platform, including the advanced version of AlphaFold, in these collaborations. AlphaFold's next generation is designed to expand its predictive capabilities beyond protein structures to encompass small molecules and nucleic acids, crucial components in drug development.

Isomorphic Labs aims to reimagine drug discovery from an AI-first perspective. The company's approach involves developing predictive and generative models of biological phenomena. These models are crucial in understanding how potential drugs interact with their targets at a molecular level, which can significantly streamline the drug design process.

The collaborations with Eli Lilly and Novartis will likely utilize these AI capabilities to identify and optimize small molecule therapeutics, a challenging aspect of drug discovery. Small molecules are often used as drugs due to their ability to modulate biological processes, but finding molecules that are both effective and safe is a complex task. AI models can predict how these molecules will behave in the body, their potential efficacy, and their safety profile.

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