Iambic Secures $100M For Drug Discovery Platform

Iambic, a clinical-stage life sciences company, has raised an oversubscribed $100 million funding round with participation from investors including Abingworth, Alexandria Venture Investments, Alumni Ventures, ARK, Ascenta, Catalio, Everbright Biofund, Freeflow Ventures, Illumina Ventures, Mubadala, Pegasus Tech Ventures, Qatar Investment Authority, Regeneron Ventures, Sequoia, Tao Capital Partners, Terra Magnum Capital Partners, and Wilson Sonsini Goodrich and Rosati.

The company integrates physics principles into its AI-driven architectures to explore chemical space more deeply, with platforms such as Enchant, NeuralPLexer, and OrbNet. Enchant is a multimodal transformer model that combines limited clinical data with lab data to predict human pharmacokinetics and other clinical drug properties early in discovery. NeuralPLexer predicts protein-ligand complex structures and conformational changes induced by drug molecules, while OrbNet applies AI-accelerated quantum chemistry through a graph neural network built on quantum features. The new funding will help advance these technology platforms.

The funding announcement follows Iambic’s presentation of clinical data for IAM1363 at the European Society for Medical Oncology Congress in Germany, showing promising anti-tumor activity and safety in HER2-wild-type and HER2-mutated cancers. Iambic also recently partnered with Jazz Pharmaceuticals, which provided zanidatamab (Ziihera), a HER2-targeted bispecific antibody, for combination evaluation with IAM1363 in patients with HER2-positive breast cancer previously treated with trastuzumab deruxtecan (Enhertu).

In July, Iambic entered a technology and research collaboration with Revolution Medicines to develop novel drug candidates using its AI models. Revolution Medicines is providing molecular libraries and structural data to train custom versions of NeuralPLexer, while gaining access to Iambic’s PropANE model, a pre-trained graph neural network used to predict and optimize drug properties during lead selection.