Insitro Partners with Eli Lilly to Develop Advanced Machine Learning Models for Small Molecule Drug Discovery

Strategic Insights -

Insitro, a pioneer in machine learning for drug discovery and development, has announced a new collaboration with Eli Lilly and Company (Lilly) to develop advanced machine learning models that can predict key pharmacological properties of small molecules. This effort aims to address longstanding challenges in drug development where such properties have traditionally been slow and costly to determine through experimental methods in the lab. The collaboration will combine insitro's advanced computational expertise with Lilly's extensive drug discovery data to build next-generation models for small molecule design.

Key Takeaways:

  • Insitro will build advanced machine learning models and train them on Lilly's proprietary preclinical data, leveraging a rich set of in vitro and in vivo measurements from a vast array of compounds.
  • The collaboration aims to accelerate the design of compounds with favorable ADMET/pharmacokinetic profiles, reducing timelines and the number of in vivo studies required.
  • The models will be used to improve the efficiency of hit-to-lead and lead optimization efforts by predicting multiple ADMET properties, including the in vivo behavior of small molecules.
  • The machine learning models developed by insitro will be available to insitro and Lilly, as well as their partners, including biotech companies that partner with Lilly TuneLab.
  • The partnership expands the relationship between insitro and Lilly, announced in 2024, which focuses on Lilly's siRNA delivery and antibody discovery capabilities to enable insitro's emerging pipeline in metabolic diseases.
  • The collaboration will be part of the Lilly Catalyze360 model, a comprehensive approach to empower early-stage biotechs, and will leverage a federated learning infrastructure hosted by a third-party provider.
  • The novel ADMET models will be a critical component of insitro's end-to-end AI capability for small molecule chemistry, including machine learning and physics-based in silico screening, affinity machine learning models from proprietary DNA-encoded libraries, and an active learning medicinal chemistry engine.

Statistics:

  • Over $700 million in capital raised to date for insitro.
  • Insitro is building a "pipeline through platform" with a focus on metabolic disease and neuroscience.
  • The collaboration aims to reduce timelines and the number of in vivo studies required for drug development.
  • The machine learning models developed by insitro will be available to insitro and Lilly, as well as their partners.

Sources:

  • Insitro (2024) - "insitro and Lilly Expand Relationship to Enhance Metabolic Disease Pipeline"
  • Insitro (2024) - "Lilly TuneLab to Use insitro's Machine Learning Models to Accelerate Small Molecule Discovery"
  • Insitro (2024) - "insitro Raises Over $700 Million in Capital to Drive AI-Enabled Drug Discovery"
  • Contify.com (Copyright 2017) - "Insitro Partners with Eli Lilly to Develop Advanced Machine Learning Models for Small Molecule Drug Discovery"