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.
De-risk discovery & harness the power of AI to bring life-saving drugs to patients faster.
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.
Discover a groundbreaking webinar delving into the use of artificial intelligence (AI) and machine learning (ML) in preclinical drug research and related regulatory frameworks. The challenges and opportunities of integrating AI into preclinical drug research and development workflows are of increasing importance, especially in the context of regulatory science expectations. On the one hand, legislative priorities seek to reduce the cost of healthcare and drugs, while also reducing the burden of testing on animals through the use of technology like AI.
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.
Download this webinar recording with VeriSIM Life CEO & Founder Dr. Jo Varshney and Dr. Jeff Barrett, CSO at Aridhia, to learn: What is model-informed drug development (MIDD); a high level level paradigm for yielding outputs from data inputs; role of machine learning in a "fused" framework; and explainability.
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.
2 min video with VSL CEO & Founder Jo Varshney where she describes the BIOiSIM platform that integrates machine learning with robust models of in vivo pharmacokinetics and pharmacodynamics, enabling faster model development, more accurate prediction, and higher scalability.
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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