Cancer drug candidate developed using supercomputing & AI blocks tumor growth without toxic side effect

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In a milestone for supercomputing-aided drug design, Lawrence Livermore National Laboratory and BridgeBio Oncology Therapeutics today announced clinical trials have begun for a first-in-class medication that targets specific genetic mutations implicated in many types of cancer. The drug discovery work was powered by LLNL supercomputers Ruby (shown), Quartz and Lassen. Graphic by Amanda Levasseur.

A new cancer drug candidate developed by Lawrence Livermore National Laboratory (LLNL), BBOT (BridgeBio Oncology Therapeutics) and the Frederick National Laboratory for Cancer Research (FNLCR) has demonstrated the ability to block tumor growth without triggering a common and debilitating side effect.

The discovery of BBO-10203 brings together DOE high-performance computing with AI and biomedical expertise to accelerate drug discovery. LLNL is leveraging its Livermore Computer-Aided Drug Design (LCADD) platform — combining AI and machine learning with physics-based modeling — and world-class DOE supercomputing resources like Ruby and Lassen, to simulate and predict drug behavior long before any compound is synthesized.