VeriSIM Life partnered with our client to translate drug candidates targeting SUD patient therapies more rapidly and accurately than traditional methods that depend on the experimental uncertainty of modeling for data. In the first stage of the collaboration, VeriSIM Life’s hybrid AI platform, BIOiSIM, was used to predict pharmacokinetic (PK) properties necessary for the selection of potentially viable drug lead candidates. A PK model was developed to determine brain disposition of the drug molecules.
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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