Iambic Therapeutics

Define strict target product profile parameters requiring brain penetrance and high selectivity.

Utilize physics-based AI to map the structural environment of the HER2 target.

Generate and filter compounds mathematically to achieve over 5000-fold selectivity.

Synthesize candidates via the automated lab and validate in-vivo models to select the Phase 1 clinical candidate.

Model the structural differences between target kinases and off-target kinome proteins.

Employ the multi-objective generative AI to design molecules that bind exclusively to CDK2/4.

Run parallel high-throughput biological screens to verify absence of binding to off-target receptors.

Iterate weekly to refine the chemical structure until toxicity profiles are minimized.

Deploy molecular dynamic simulations to visualize protein movement over time.

Identify transient 'cryptic pockets' that appear during structural fluctuations.

Design custom small molecules intended to lock into these transient pockets to inhibit PPIs.

Conduct rigorous biophysical assays in the high-throughput platform to confirm functional allosteric inhibition.

Ingest comprehensive preclinical datasets (genomic, phenotypic) into the Enchant Multi-Modal Transformer Model.

Process multi-modal inputs to uncover hidden biological linkages with historical clinical data.

Identify and validate translational biomarkers predictive of human efficacy.

Inform clinical trial design and patient selection criteria based on model outputs.

Establish joint steering committee with the partner (e.g., Takeda) to set target objectives.

Feed the partner’s proprietary target data into Iambic's AI discovery platform.

Rapidly explore the chemical space to generate diverse, high-quality leads.

Deliver optimized, differentiated clinical candidates at a pace years faster than standard internal development.