This discussion between VeriSIM Life and experts at Pfizer, Bayer and Janssen explores how adopting AI and deep technology to the R&D process reduces costs and accelerates time to market whilst maintaining safety.
This discussion between VeriSIM Life and experts at Pfizer, Bayer and Janssen explores how adopting AI and deep technology to the R&D process reduces costs and accelerates time to market whilst maintaining safety.
Key themes include:
• Identifying the most common barriers preventing the successful adoption of AI initiatives for drug development in small and large organizations
• Comparing the challenges with applying AI to determine safety and efficacy: Small molecule vs. large molecule candidates
• Understanding how general and deterministic systems approach PK/PD/E predictions differently: What are the impacts?
• AI adoption and the impacts of user interfaces (UI and UX) on different user groups
• Overcoming data capture, accessibility and analytic challenges: Ensuring availability of accurate, usable data for generation and prediction
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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