Download this webinar recording with VeriSIM Life CEO & Founder Dr. Jo Varshney and Dr. Jeff Barrett, CSO at Aridhia, to learn: What is model-informed drug development (MIDD); a high level level paradigm for yielding outputs from data inputs; role of machine learning in a "fused" framework; and explainability.
Drug developers increasingly incorporate computational/in silico solutions to reduce costs and improve translation from the bench to the clinic. But even with these tools, challenges persist in determining efficacy, disease pathways, mechanism of action, toxicity, best route of administration and ideal formulation.
Now, predictive AI and machine learning techniques are filling gaps previously out of reach to traditional approaches.
View this recorded discussion with VeriSIM Life CEO & Founder Dr. Jo Varshney and Dr. Jeff Barrett, CSO at Aridhia, to learn:
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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