On Jun. 3, 2026, Alnylam Pharmaceuticals, the leading RNAi therapeutics company, and Inceptive Nucleics, which builds foundation models of life, announced a strategic collaboration agreement to increase the pace of therapeutic innovation. The collaboration is valued at up to $2 billion with upfront consideration of $30 million, including cash and the purchase of Inceptive equity. Inceptive is eligible to receive additional payments based on the achievement of preclinical, regulatory, and commercial sales milestones.

By integrating Inceptive’s generative AI models with Alnylam’s R&D engine, Alnylam aims to accelerate the discovery of novel RNAi therapeutics as it advances ambitious pipeline expansion goals as part of its Alnylam 2030 strategy.

Inceptive’s foundation model learns the patterns underlying biology and hence can adapt to diverse therapeutic modalities without retraining. In joint exploratory work, the model achieved exceptional performance within weeks, uncovering meaningful biological insights from relatively small datasets to characterize siRNA molecules, the active ingredient in RNAi therapeutics.

The collaboration pairs Alnylam’s RNAi leadership with Inceptive’s foundation models and AI expertise to catalyze and accelerate progress in nucleic-acid based drug design. Inceptive focuses on developing models for sequence-based medicines such as RNAi therapeutics, which were pioneered by Alnylam.

Alnylam’s platform: An R&D engine that has produced six approved drugs, backed by 20+ years of proprietary siRNA data.

Inceptive’s foundation models of life: AI models for sequence-based medicines that generalize across programs and continuously improve.

The collaboration also seeks to advance siRNA design, by modeling target mRNAs and jointly exploring sequence space and novel chemical modifications to enhance potency and efficacy, and by predicting top-performing therapeutic candidates in preclinical models for further development by Alnylam. The goal is to help Alnylam prioritize the most promising molecules and improve experimental productivity.