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
Dr. Jo Varshney, leader of artificial intelligence and machine learning drug development platform BIOiSIM, is recognized as Most Innovative Woman of the Year in the Stevie Awards Healthcare category.
Dr. Jo Varshney, leader of artificial intelligence and machine learning drug development platform BIOiSIM, is recognized as Most Innovative Woman of the Year in the Stevie Awards Healthcare category.
VeriSIM Life announces recognition for continued advances with BIOiSIM, a first-in-class virtual drug development engine.
VeriSIM Life announces recognition for continued advances with BIOiSIM, a first-in-class virtual drug development engine.
.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)
