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Insilico Medicine Announces US$888 Mln Multi-Year Oncology Collaboration With Servier

By RTTNews Staff Writer   ✉  | Published:  | Google News Follow Us  | Join Us

Insilico Medicine announced that it has entered into a multi-year research and development (R&D) collaboration with Servier, an independent international pharmaceutical company governed by a foundation. The partnership, valued at up to US$888 million, is focused on discovering and developing innovative oncology therapies by combining Insilico's AI-driven drug discovery platforms with Servier's global expertise in cancer drug development.

Under the terms of the agreement, Insilico will be eligible to receive up to US$32 million in upfront and near-term R&D payments. The company will leverage its proprietary AI-powered technologies to identify and advance potential drug candidates that meet predefined scientific and development criteria. Servier will share research and development costs and, upon successful identification of promising candidates, will lead clinical validation, regulatory interactions, and worldwide commercialization.

This strategic alliance underscores Insilico's leadership in AI-driven oncology drug discovery. The company has established a robust pipeline targeting multiple cancer indications, including ISM6331, a potential best-in-class pan-TEAD inhibitor, and ISM3412, a MAT2A inhibitor—both currently in global, multicenter Phase I clinical trials. In addition, four other oncology programs have been fully or partially out-licensed to partners, with Phase I trials actively in progress.

By harnessing advanced AI and automation technologies, Insilico has significantly accelerated preclinical drug development timelines. While traditional early-stage drug discovery typically requires an average of 4.5 years, Insilico has nominated 20 preclinical candidates between 2021 and 2024. Each program achieved preclinical candidate (PCC) nomination within just 12 to 18 months, with only 60 to 200 molecules synthesized and tested—setting a new benchmark for efficiency in AI-driven drug R&D.

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