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Bringing a new drug to market costs far too much and takes far too long — and for cancer treatments especially, there is inherent urgency around getting these life-saving medications to patients faster. Antibody-drug conjugates show tremendous promise as a more selective therapy modality. But, as the industry investigates more conjugates and payload combinations, the expansion of preclinical research and experimentation is exploding. Download the recording to learn more.
Bringing a new drug to market costs far too much and takes far too long — and for cancer treatments especially, there is inherent urgency around getting these life-saving medications to patients faster. Antibody-drug conjugates show tremendous promise as a more selective therapy modality. But, as the industry investigates more conjugates and payload combinations, the expansion of preclinical research and experimentation is exploding.
Can adding artificial intelligence (AI) to the drug discovery and development process help manage the combinatory scale challenge posed by antibody-drug conjugates? Are all AI approaches the same? What challenges will teams face and how can they be overcome when adopting different AI approaches to their research and development plans? What can drug developers realistically expect in terms of worthwhile outcomes?
In this webinar, the expert speakers discussed how VeriSIM Life and Debiopharm partnered to scalably investigate first-in-human (FIH) dosing strategies for antibody-drug conjugates to reduce tumor burden safely and effectively. Topics explored will include:
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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