The average discovery phase of drug development lasts four and half years with just six percent of evaluated compounds moving into the preclinical development phase as candidates. Improving success rates in drug discovery by just two percent represents significant downstream savings throughout the entire clinical development process across both direct costs and time.
In-silico drug discovery methods rely heavily on informatics and analytics that help digitize more parts of the discovery process from target identification through lead screening and optimization. Today, there is an opportunity for scientists to discover the best new chemical leads faster through effective use of technology platforms along with machine learning and generative AI applications that speed time to insight, increase collaboration, and support data driven decision making.
Certara, a global leader in model-informed drug development recently acquired Chemaxon, a leading provider in cheminformatics software. Used by research scientists globally, Chemaxon software helps to digitize the design, make, test and analyze (DMTA) lifecycle to discover the best new chemical leads. Together, Certara and Chemaxon offer a more integrated and comprehensive data and predictive analytics platform, improving decision-making from discovery through commercialization.
In this webinar, Certara CEO Dr. William Feehery, and Chemaxon President Dr. Richard Jones, cover how a model informed discovery approach can be used to better predict successful outcomes and positively impact the entire development lifecycle.
What you’ll learn:
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- Current approaches to using biosimulation in drug discovery and development today
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- Opportunities to further utilize advance model informed drug discovery and development to predict lead success
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- Methods to streamline the design, make, test, analyze (DMTA) process from lead identification through optimization
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- Optimal workflows that reduce data silos across functions