Insilico Medicine has announced a multi-year research and development collaboration with independent international pharmaceutical company Servier to identify and develop novel oncology therapeutics for difficult-to-drug targets using Insilico’s Pharma.AI platform.
The partnership will integrate advanced AI technologies into early-stage drug discovery, with Insilico leading AI-driven discovery and development of candidates that meet predefined criteria and Servier sharing R&D expenses and taking responsibility for clinical validation and commercialization.
Under the agreement Insilico is eligible to receive up to US$32 million in upfront and near-term R&D payments. The structure pairs Insilico’s AI-enabled discovery capabilities with Servier’s global experience in clinical development and bringing medicines to market.
“This collaboration underscores Servier’s commitment to applying cutting-edge technologies to address unmet medical needs for the benefit of patients and reflects our confidence in Insilico’s internally developed and validated AI platform,” said Christophe Thurieau, Executive Director of Research at Servier.
Insilico framed the deal as further validation of its approach to integrating generative AI across the pharmaceutical value chain. “As we deepen the integration of generative AI into every stage of the pharma value chain, I believe the future of pharmaceutical superintelligence is never so close,” said Dr Alex Zhavoronkov, founder, CEO and CBO of Insilico Medicine.
Insilico brings an oncology-focused pipeline to the collaboration, including the potential best-in-class pan-TEAD inhibitor ISM6331 and the MAT2A inhibitor ISM3412, both in global, multicentre Phase I trials. Four additional oncology programmes have been fully or partially out-licensed to partners, with Phase I trials under way.
The company highlighted efficiency gains in preclinical development achieved by combining AI and automation. Between 2021 and 2024 Insilico nominated 20 preclinical candidates, with an average timeline of 12 to 18 months from project initiation to candidate nomination, compared with a typical early-stage discovery timeline of about 4.5 years. Each programme required synthesis and testing of only 60 to 200 molecules, illustrating the potential of AI to accelerate oncology drug R&D.
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