A UC San Francisco cancer researcher has led a team of data scientists and engineers to win a first-of-its-kind Artificial Intelligence (AI) Genomics Hackathon competition.
A UC San Francisco cancer researcher has led a team of data scientists and engineers to win a first-of-its-kind Artificial Intelligence (AI) Genomics Hackathon competition.
Collaboration to reduce the number of outsourced preclinical experiments and help de-risk R&D decisions early-on to ensure expedited development of novel, safe and highly efficacious drugs
Collaboration to reduce the number of outsourced preclinical experiments and help de-risk R&D decisions early-on to ensure expedited development of novel, safe and highly efficacious drugs
VeriSIM Life today announced it has been selected as the winner of the “Predictive Analytics Solution of the Year" in the BioTech Breakthrough Awards program conducted by BioTech Breakthrough.
VeriSIM Life today announced it has been selected as the winner of the “Predictive Analytics Solution of the Year" in the BioTech Breakthrough Awards program conducted by BioTech Breakthrough.
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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-ligand 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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