
Small Molecule Therapy Developer



The proceeds from the latest financing will be used to advance Iambic's clinical and preclinical programs. These include IAM1363, a highly selective brain-penetrant small molecule inhibitor currently in Phase 1/1b study for wild-type and oncogenic HER2 mutations, and a potential first-in-class selective dual CDK2/4 inhibitor designed to expand the therapeutic window and address treatment resistance in solid tumors across various cancers.

Thomas Miller, Ph.D., CEO of Iambic Therapeutics, Inc., said:"We are thrilled to build a robust team of investors around the company who share our belief that Iambic Therapeutics' AI-driven technology platform can deliver highly differentiated medicines to clinics."

Iambic Therapeutics' candidate pipeline was discovered using its leading AI models for protein structure prediction and integrated drug design. These technologies are integrated into a closed-loop, automated, high-throughput biology and chemistry experimentation platform, which delivers new biological insights weekly from thousands of molecular designs, further informing its AI models directly.
Mubadala Capital Partner Ayman AlAbdallah stated:"Iambic is a true innovator, both in terms of the accuracy and speed of its state-of-the-art AI drug discovery models and in its ability to rapidly advance drug candidates from discovery to human studies. Iambic is a company specifically built for AI and drug discovery, and we are excited to see how their expertise in machine learning and drug hunting will help fulfill the promise of AI to bring potentially life-saving drugs to patients."

Iambic Therapeutics Physics Informed AI-Driven Discovery Platform
Iambic Therapeutics' AI-driven platform aims to address the most challenging design problems in drug discovery, integrating Iambic's latest artificial intelligence technologies and purpose-built tools. Incorporating physical principles into the platform’s AI architecture enhances data efficiency and enables molecular models to extensively explore the possible chemical structure space. The platform’s algorithms can identify novel chemical mechanisms to engage difficult biological targets, discover defined product profiles that optimize therapeutic windows, and explore chemical space to identify development candidates with highly differentiated properties. By tightly integrating AI-generated molecular designs with automated experimental execution, Iambic completes design-make-test cycles on a weekly basis.

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