.jpeg)
We’ve all heard the hype around artificial intelligence, especially its application for drug discovery and development. In some corners, it’s coming to replace jobs. In others, it’s going to revolutionize life science and the pharmaceutical industry. But what do drug developers think about using AI? Are established companies ready to incorporate AI for success and competitive advantage? If so, how are they doing it today? Join this fireside chat with the featured speakers to understand how established companies are approaching AI and integrating it into their strategy and research and development (R&D) workflows.
We’ve all heard the hype around artificial intelligence, especially its application for drug discovery and development. In some corners, it’s coming to replace jobs. In others, it’s going to revolutionize life science and the pharmaceutical industry. But what do drug developers think about using AI? Are established companies ready to incorporate AI for success and competitive advantage? If so, how are they doing it today? Join this fireside chat with the featured speakers to understand how established companies are approaching AI and integrating it into their strategy and research and development (R&D) workflows.
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.
.png)
