San Francisco, CA – November 11, 2024 – VeriSIM Life, a pioneer in AI-driven drug development, is thrilled to announce it has won a Gold Stevie® Award for Most Innovative Company of the Year in the Stevie Awards for Women in Business. The Stevie Awards are widely recognized as the premier business accolades worldwide, celebrating the outstanding achievements of women leaders, entrepreneurs, and companies they lead. Dr. Jo Varshney founded VeriSIM Life in 2017 to transform the drug development landscape, and this honor reflects the company’s commitment to bringing life-saving therapies to patients more efficiently.
San Francisco, CA – November 11, 2024 – VeriSIM Life, a pioneer in AI-driven drug development, is thrilled to announce it has won a Gold Stevie® Award for Most Innovative Company of the Year in the Stevie Awards for Women in Business. The Stevie Awards are widely recognized as the premier business accolades worldwide, celebrating the outstanding achievements of women leaders, entrepreneurs, and companies they lead. Dr. Jo Varshney founded VeriSIM Life in 2017 to transform the drug development landscape, and this honor reflects the company’s commitment to bringing life-saving therapies to patients more efficiently.
SAN FRANCISCO, Oct. 1, 2024 /PRNewswire-PRWeb/ -- VeriSIM Life, a leader in AI-driven drug discovery and development, today announced its second collaboration agreement with Mayo Clinic, aimed at accelerating the development of new therapies for liver and metabolic diseases. The collaboration will leverage Mayo Clinic's extensive clinical and research expertise along with VeriSIM Life's pioneering AI-powered platform to address critical unmet medical needs.
SAN FRANCISCO, Oct. 1, 2024 /PRNewswire-PRWeb/ -- VeriSIM Life, a leader in AI-driven drug discovery and development, today announced its second collaboration agreement with Mayo Clinic, aimed at accelerating the development of new therapies for liver and metabolic diseases. The collaboration will leverage Mayo Clinic's extensive clinical and research expertise along with VeriSIM Life's pioneering AI-powered platform to address critical unmet medical needs.
San Francisco, CA – September 23, 2024 – VeriSIM Life was named a finalist in the Most Innovative Company of the Year category in the 21st annual Stevie® Awards for Women in Business. The Stevie Awards for Women in Business honor women executives, entrepreneurs, employees and the companies they run, worldwide. VeriSIM Life was founded in 2017 by Dr. Jo Varshney. The Stevie Awards have been hailed as the world’s premier business awards.
San Francisco, CA – September 23, 2024 – VeriSIM Life was named a finalist in the Most Innovative Company of the Year category in the 21st annual Stevie® Awards for Women in Business. The Stevie Awards for Women in Business honor women executives, entrepreneurs, employees and the companies they run, worldwide. VeriSIM Life was founded in 2017 by Dr. Jo Varshney. The Stevie Awards have been hailed as the world’s premier business awards.
.png)
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.
.png)
