Iambic Therapeutics Leverages Lambda’s NVIDIA HGX B200 Cluster to Enhance AI-Driven Drug Discovery with Enchant v2

Lambda, a prominent GPU cloud provider, has announced that Iambic Therapeutics, a clinical-stage life science and technology company, has chosen Lambda to supply an NVIDIA HGX B200 cluster to enhance the training of its advanced AI molecular property prediction model, Enchant. This move underscores Iambic's commitment to developing more effective medicines and accelerating the drug discovery process. Iambic's Enchant model is a cutting-edge, multi-modal transformer designed to predict various clinical and preclinical endpoints, such as biological, physiochemical, pharmacokinetic, metabolic, safety, and other properties crucial for the success of drug candidates. Enchant v2, recently released, showcases significant improvements in accuracy and scalability, making high-confidence predictions even in scenarios with limited data. This capability is particularly valuable in the early stages of drug discovery, where understanding potential patient impacts is critical. Enchant v2 has been benchmarked against competing models and consistently performs at the forefront of the industry, often outperforming traditional in vitro experiments in predicting in vivo drug clearance. Such advancements are vital as regulatory bodies increasingly look for broader use of in silico testing. Matt Welborn, PhD, Iambic’s Vice President of Machine Learning, emphasized that the company aims to capitalize on the model's performance gains by further scaling its computational resources. The addition of Lambda’s NVIDIA HGX B200 cluster will significantly boost Iambic's ability to expand the range and depth of preclinical and clinical endpoint predictions, ultimately enhancing the efficiency of drug development and increasing the likelihood of successful human trials. Robert Brooks IV, Founding Team and Vice President of Revenue at Lambda, highlighted the deepening partnership with Iambic. Lambda’s 1-Click Clusters allow Iambic to rapidly test and validate models, facilitating a seamless transition to the new, powerful NVIDIA HGX B200 cluster. This partnership exemplifies how cloud-based AI compute can revolutionize drug discovery by providing the necessary infrastructure for complex and computationally intensive tasks. Iambic’s AI-driven discovery platform integrates advanced technologies like Enchant and NeuralPLexer, which predicts protein and protein-ligand structures with high accuracy. By incorporating physics principles into its AI architectures, the platform enhances data efficiency and explores a broader range of chemical structures. This rapid, iterative design-make-test cycle, occurring on a weekly basis, accelerates the identification of novel drug candidates and optimizes their therapeutic potential. Iambic’s platform has already demonstrated the ability to deliver new drug candidates to human clinical trials swiftly and is advancing a robust pipeline of potential best-in-class and first-in-class clinical assets to address pressing patient needs. Lambda, founded in 2012 by AI engineers, focuses on providing top-tier AI compute solutions for developers. Its product lineup includes on-prem GPU hardware and cloud-hosted GPUs, catering to all stages of the AI development lifecycle. Lambda’s mission is to make AI computation as accessible and efficient as electricity, empowering researchers and developers to drive innovation in fields like drug discovery and beyond. Industry insiders view this collaboration as a strategic move that could redefine the landscape of AI in pharmaceutical research. The combination of Iambic's expertise in AI-driven drug discovery and Lambda’s powerful computational resources positions both companies to make significant contributions to the field, potentially leading to faster and more effective drug development processes. This partnership aligns with the broader trend of integrating AI and cloud computing to solve complex biological challenges, highlighting the growing reliance on technology to advance medical research and treatment.