VeriSIM Life utilized its hybrid AI platform, BIOiSIM, to address the challenges of limited and missing data from clinical trials. The approach included imputing missing biomarker data utilizing hybrid AI models to reduce missing values with high robustness and enriching patient populations, including underrepresented groups, through AI-driven virtual patient generation (with 10,000 virtual patients generated). The use of such enriched data also allowed for the use of Explainable AI to identify the most important biomarkers - enabling accurate patient stratification.
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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