San Francisco, CA - August 21, 2026 - VeriSIM Life has been named a finalist in the 2026 CPHI Awards Innovation Catalyst category, recognizing its work to solve one of drug development’s hardest problems: determining which drug candidates are most likely to translate successfully into humans.
San Francisco, CA - August 21, 2026 - VeriSIM Life has been named a finalist in the 2026 CPHI Awards Innovation Catalyst category, recognizing its work to solve one of drug development’s hardest problems: determining which drug candidates are most likely to translate successfully into humans.
Notice of Allowance strengthens PulmoSIM's therapeutic portfolio while validating VeriSIM Life's platform for advancing therapeutics from design to the clinic.
Notice of Allowance strengthens PulmoSIM's therapeutic portfolio while validating VeriSIM Life's platform for advancing therapeutics from design to the clinic.
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.
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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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