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.
While much of AI drug discovery focuses on generating molecules or predictions, VeriSIM Life’s BIOiSIM® platform focuses on human translation. BIOiSIM combines mechanistic biology, biosimulation and explainable AI to evaluate not only what is predicted to happen, but why, where uncertainty remains, and whether the underlying biology is likely to hold in patients.
“Drug development does not suffer from a shortage of molecules or predictions. The challenge is knowing which ones will actually translate into humans,” said Dr. Jo Varshney, Founder and CEO of VeriSIM Life. “We built BIOiSIM to understand that transition and give teams greater confidence in which drugs to advance.”
BIOiSIM evaluates efficacy, exposure, safety, dosing, formulation and translational risk, helping development teams make critical decisions earlier, before committing years and significant capital to the wrong candidate.
As AI becomes increasingly common across pharmaceutical R&D, VeriSIM Life believes the next differentiator will not be who can generate the most predictions, but who can determine which predictions are biologically credible and likely to translate into medicines.
The winners of the 2026 CPHI Awards will be announced October 6, 2026, in Milan, Italy.
VeriSIM Life develops BIOiSIM®, a translational intelligence platform combining mechanistic biology, biosimulation and explainable AI to help drug developers determine which candidates are most likely to translate successfully into humans.
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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