In this free webinar, 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.

TORONTO, Aug. 10, 2023 /PRNewswire-PRWeb/ — Drug developers are increasingly turning to computational / in silico tools and methods to enhance novel drug investigation, as well as reduce costs and improve translation throughout the development lifecycle, from lead optimization through validation.

But despite the infusion of computational models and the improvements gained from their use, the drug failure rate remains the same (if not greater). Workflows leveraging key datasets are often siloed, as are traditional computational models, which also suffer from being overfit, error-prone and limited in their extensibility.

Enter artificial intelligence (AI) and increasing enthusiasm about the application of AI in drug development. A recent survey from industry tracker Linus Group found 38 percent of respondents in biopharma/pharma say adopting machine learning or AI is a top priority for this year. Why?

Integrating computational models and drug intelligence datasets at different R&D stages, along with adding machine learning (ML) to understand those interactions enables better predictions and identifies potential failure points early in a program lifecycle.

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.

Join 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. 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.

Join Dr. Jo Varshney, PhD, DVM, CEO, VeriSIM Life; and John Earl, PhD, Sr. Director, Solutions Consulting, Life Sciences & Healthcare, Clarivate, for the live webinar on Friday, September 1, 2023, at 11am EDT (4pm BST/UK).

For more information, or to register for this event, visit Using AI in Drug Development to Enhance Preclinical Translatability.

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Pull Quote

Integrating computational models and drug intelligence datasets at different R&D stages, along with adding machine learning (ML) to understand those interactions enables better predictions and identifies potential failure points early in a program lifecycle.

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