VeriSIM Life is pioneering a transformative approach to drug discovery with its state-of-the-art AI-driven knowledge platform. Unlike traditional development methods, our platform isn’t bound by disease-specific or target limitations, enabling us to explore a vast array of therapeutic areas and accelerate the development of breakthrough treatments.
Our asset portfolio exemplifies this broad reach, targeting complex and high-need conditions such as pulmonary arterial hypertension, substance use disorder, and challenging oncology cases. With each program, we leverage our platform’s predictive accuracy to refine and prioritize the most promising candidates, minimizing time to market and clinical risk.
By expanding our pipeline with precision-driven assets across diverse diseases, VeriSIM Life is advancing a powerful, scalable approach to drug development that addresses urgent patient needs with unparalleled potential.
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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