Iambic Therapeutics Announces New Research Published in Nature Machine Intelligence Demonstrating the Capabilities of Its Generative AI NeuralPLexer Technology to Predict Protein-Ligand Complex Structures

Advances in AI Platform Provide an Ability to Rapidly Predict, Identify and Design Biomolecular Structures to Accelerate the Pace and Reduce the Cost of Drug Discovery and Development

SAN DIEGO--(BUSINESS WIRE)-- Iambic Therapeutics, a biotechnology company developing novel therapeutics using its unique generative AI discovery platform, today announced the publication of research in Nature Machine Intelligence showing that its NeuralPLexer technology outperforms other state-of-the art systems in predicting the structure of protein-ligand complexes as well as the conformational changes to these structures from the addition of drug molecules.

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A frame from the NeuralPLexer generative diffusion process, as it creates the predicted protein-ligand complex structure. (Graphic: Business Wire)

“Our benchmarking shows we have set a new standard for predicting protein-ligand binding, directly generating 3D coordinates for full binding complexes, rapidly making available new reference structures, while improving prediction accuracy for novel targets and large-scale in silico screening,” said Tom Miller, PhD, Iambic Therapeutic’s CEO and co-author of the paper. “NeuralPLexer is allowing us to discover pharmacological patterns for increasingly complex protein targets and target areas and achieve unprecedented selectivity, novel mechanisms of structural engagement, the ability to expand patient populations by adding multiple target mutations as well as identify new mechanisms of action at protein-protein interfaces and at other unspecified sites. We are now generating structures that once took many months and significant investment to generate in just a matter of seconds.”

The peer-reviewed manuscript, State-specific protein-ligand complex structure prediction with a multi-scale deep generative model, was published online today and was the result of a collaboration between scientists at Iambic, Caltech and NVIDIA. A link to the publication can be found here.

While there has been great progress in using AI-driven systems to predict 3D protein structures, NeuralPLexer is advancing the field by predicting the conformational response of proteins on ligand binding, which is essential for understanding the impact of drug molecules on protein function.

The Company today also released a white paper highlighting the improvements in its next-generation NeuralPLexer2. Trained in October 2023, NeuralPLexer2 has already demonstrated significant improvements in the technology’s prediction accuracy and has scaled the model to include most categories of biological structures adding protein-protein complexes, cofactors, post-translational modifications (PTMs), and protein-nucleic acid complexes, and encompassing almost all structures in the Protein Data Bank (PDB).

Iambic Therapeutics uses NeuralPLexer in building its own pipeline, including the discovery of IAM1363, a selective and brain-penetrant small molecule inhibitor of HER2 wildtype and oncogenic mutant proteins, designed to expand therapeutic index compared to available HER2 inhibitors and to avoid toxicities from off-target inhibition of EGFR, a related receptor tyrosine kinase. In preclinical studies, IAM1363 has demonstrated over 1000-fold selectivity for HER2 compared to EGFR. An IND for IAM1363 was recently accepted by FDA and clinical trials are planned to commence in early 2024 – a timeline that has the drug candidate moving from program start to clinical studies in under two years.

“Iambic’s NeuralPLexer2 is pushing the boundaries of generative AI in 3D protein prediction, helping to enable new capabilities by accurately representing how structures alter their shape as a result of drug interactions,” said Rory Kelleher, Global Head of Business Development for Life Sciences at NVIDIA. “These advances demonstrate the possibilities of a new era of computer-aided drug discovery that aims to accelerate the process as well as develop better drug candidates – and, as part of this movement, Iambic’s innovations are being translated into important new medicines for patients.”

About the Iambic Therapeutics Physics-Informed AI-Driven Discovery Platform

The Iambic Therapeutics AI-driven platform was created to address the most challenging design problems in drug discovery, incorporating the most current AI technologies and purpose-built tools from Iambic. The integration of physics principles into the platform’s AI architectures improves data efficiency and allows molecular models to venture widely across the space of possible chemical structures. The platform’s algorithms enable identification of new chemical mechanisms for engaging difficult-to-address biological targets, discovery of defined product profiles that optimize therapeutic window, and exploration of the chemical space to discover candidates for development with highly differentiated properties. Through close integration of AI-generated molecular designs with automated experimental execution, Iambic completes design-make-test cycles on a weekly cadence.

About Iambic Therapeutics

Founded in 2019 and headquartered in La Jolla, California, Iambic Therapeutics is disrupting the therapeutics landscape with its unique AI-driven drug-discovery platform. Iambic has assembled a world-class team that unites pioneering AI experts and experienced drug hunters with strong track records of success in delivering clinically validated therapeutics. The Iambic platform has been demonstrated to deliver high-quality, differentiated therapeutics to clinical stage with unprecedented speed and across multiple target classes and mechanisms of action. The Iambic team is advancing an internal pipeline of clinical assets to address urgent unmet patient needs. Learn more about the Iambic team, platform, and pipeline at iambic.ai.

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