Publications & Blog
Safety in Numbers: Understanding the Effectiveness of Herbals Through Better Science
Owing to my Indian roots, herbal medicine has been a part of my everyday life in the form of ayurveda. As a child, I remember my mum making a concoction of holy-basil, mint and liquorice whenever I had a bad…
Safety in Numbers: Understanding the Effectiveness of Herbals Through Better Science
Owing to my Indian roots, herbal medicine has been a part of my everyday life in the form of ayurveda. As a child, I remember my mum making a concoction of holy-basil, mint and liquorice whenever I had a bad…
Engineering at VeriSIM
I am so excited to start this next phase of my career serving as VP of Engineering at VeriSIM. I am grateful for this opportunity to be a part of this special group. We have a lot of tough, interesting…
Engineering at VeriSIM
I am so excited to start this next phase of my career serving as VP of Engineering at VeriSIM. I am grateful for this opportunity to be a part of this special group. We have a lot of tough, interesting…
.png)
Introducing AtlasGEN™ Novel Drug Designer
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.
