The road to developing new drug treatments is paved with risk. In addition to hefty time and cost considerations, preclinical research doesn’t guarantee success in clinical settings – failure rates hover somewhere in the neighborhood of 90%.
However, digital solutions in drug development have transformative potential when it comes to pharma research and development (R&D).
Download the white paper by VeriSIM Life Chief Scientific Officer Dr. Szczepan Baran and Peter Henstock, Machine Learning & AI Lead at Pfizer, to begin advancing the adoption of AI within your organization – from closing the translational gap to better AI benchmarking and more.
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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