药物开发的平均发现阶段持续四年半，仅有 6% 的评估化合物进入候选药物的临床前开发阶段。将药物研发的成功率提高 2%，就意味着在整个临床开发过程中节省了大量的下游直接成本和时间。

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:

• • Current approaches to using biosimulation in drug discovery and development today

• • Opportunities to further utilize advance model informed drug discovery and development to predict lead success

• • Methods to streamline the design, make, test, analyze (DMTA) process from lead identification through optimization

• • Optimal workflows that reduce data silos across functions