Download the recording to learn how exactly AI and ML techniques are taking computational modeling and informatics to new domains of applicability in the drug development lifecycle. Through illustrative examples of AI in drug development, attendees will gain a better understanding of how these techniques can be applied to enhance decision-making and increase the chances of clinical success.
Increasing enthusiasm about the application of AI in drug development has made adopting deep technology a top priority for this year for biopharma/pharma. Predictive AI and ML techniques are well suited to extend to complexities like drug combinations, predicting toxicities, patient responsiveness across multiple modalities, translational differences across animal species in relation to later human effect and biological pathway discovery.
When integrated with molecular and pharmacovigilance data, these techniques provide actionable insights that can guide novel candidate design, limit unnecessary experimentation, improve candidate safety confidence and increase the return on program investment.
Watch this webinar to learn how exactly AI and ML techniques are taking computational modeling and informatics to new domains of applicability in the drug development lifecycle.
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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