Material Transfer Agreement with FDA establishes a formal framework for collaboration, reinforcing BIOiSIM’s role as a scientifically defensible, mechanistically grounded AI platform designed for the rigor of translational drug development.
Material Transfer Agreement with FDA establishes a formal framework for collaboration, reinforcing BIOiSIM’s role as a scientifically defensible, mechanistically grounded AI platform designed for the rigor of translational drug development.
San Francisco, CA — December 3, 2025 — VeriSIM Life, a leading innovator in AI-driven biosimulation and translational intelligence, today announced that it has been named the winner of the AI in Action Award at the 2025 Reuters Global Pharma Awards, an international program recognizing the most impactful advances shaping the future of the life sciences industry.
San Francisco, CA — December 3, 2025 — VeriSIM Life, a leading innovator in AI-driven biosimulation and translational intelligence, today announced that it has been named the winner of the AI in Action Award at the 2025 Reuters Global Pharma Awards, an international program recognizing the most impactful advances shaping the future of the life sciences industry.
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AtlasGEN Novel Drug Designer efficiently identifies new molecules based on favorable chemical properties and simultaneously evaluates them for clinical safety and effectiveness based on biology using VeriSIM Life’s groundbreaking Translational Index™ technology.
Iterative ranking of target engaging hits by their Translational Index values reduces the time and cost associated with other computational hit discovery approaches, while compressing the hit-to-lead refinement and optimization process by pre-validating hits for translatability. This unique integration dramatically reduces the number of compounds to evaluate in subsequent experimental research.
The ability to generate target engaging compound hits depends largely on the size of the molecular search library. The AtlasGEN search space is supported by more than 1 trillion compounds. Generative AI and deep learning techniques deliver unprecedented discovery capability.
AtlasGEN accurately predicts protein-ligand binding affinity by combining geometric conformer analysis with machine learning based on experimental data 72% more efficiently than molecular docking-based approaches.
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