Successes and Challenges in Crystal Structure Prediction (CSP) in Discovery Drug Design and Developability Assessment

Using advances in computational chemistry, data science, predictive tools and machine learning, the ultimate goal of drug discovery scientists is to significantly reduce the number of molecules that are currently made in the labs and reduce the staggering cost of drug development, reduce reliance on animal studies and speed up the discovery process. However, to achieve this goal medicinal chemists, and other drug discovery scientists, need to combine modelling to improve drug efficacy and safety with modelling to produce a developable molecule with favorable solubility, stability and other physical chemical properties. In this presentation we are going to explore the challenges and the successes we encountered applying prediction and modelling at the early stages of drug development in order to influence the compound’s design and its developability.

Faraj Atassi, Director of Pharmaceutical Development, AstraZeneca