VeriSIM Life, the leading AI-enabled, R&D decisions de-risker for drug development, today announced its Founder and CEO, Dr. Jo Varshney, has been named to the San Francisco Business Times' 2024 list of Most Influential Women in Bay Area Business. Dr. Varshney was competitively selected from more than 400 nominees to be honored with this prestigious award for her successful leadership at VeriSIM Life and innovation in the biopharma industry.
VeriSIM Life, the leading AI-enabled, R&D decisions de-risker for drug development, today announced its Founder and CEO, Dr. Jo Varshney, has been named to the San Francisco Business Times' 2024 list of Most Influential Women in Bay Area Business. Dr. Varshney was competitively selected from more than 400 nominees to be honored with this prestigious award for her successful leadership at VeriSIM Life and innovation in the biopharma industry.
Pharma industry’s first platform to design clinical success into the discovery of new drugs from day one.
Pharma industry’s first platform to design clinical success into the discovery of new drugs from day one.
New AI-enabled, integrated workflow provides pharma and biotech companies with comprehensive R&D insights to help minimize late-stage failures during clinical trials
New AI-enabled, integrated workflow provides pharma and biotech companies with comprehensive R&D insights to help minimize late-stage failures during clinical trials
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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-lygand 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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