Perspectives on Generative Chemistry — Potential and Reality
Have advances in AI and deep learning reached a threshold whereby generative chemistry methods are redefining drug design? In this webinar, our panel of experts discuss their experiences of method development, real-life application and the advances being made.
Nikolaus Stiefl, Director of Data Science at Novartis shares his experience gained whilst building a generative chemistry platform, including algorithms, backend and initial project applications. The use cases provide different insights into the pros and cons of this approach along with optimisation techniques.
Philip Cheung, Scientific Informatics Software Consultant at Collaborative Drug Discovery, explores some of the challenges faced when implementing novel machine learning models for generative chemistry – plus several interesting approaches taken to overcome them.
Matt Segall, CEO at Optibrium, discusses the challenges of applying generative chemistry methods in practice and describes how experienced chemists can guide generative methods to rigorously explore a wide range of optimisation strategies and more effectively target high-quality compounds for their projects.
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INTERESTED IN GENERATIVE METHODS?
With its comprehensive suite of integrated software, StarDrop™ delivers best-in-class in silico technologies within a highly visual and user-friendly interface. StarDrop enables a seamless flow from the latest data through predictive modelling to decision-making regarding the next round of synthesis and research. This improves the speed, efficiency, and productivity of the drug optimisation and discovery process.
Inspyra™ is just one of the range of optional StarDrop modules. Inspyra combines your expert chemistry knowledge and the exploratory power of generative methods to help you identify optimal compounds faster. As you work, Inspyra generates new compound ideas in the background while dynamically learning from your interactions. Your responses to Inspyra’s suggestions guide generative chemistry algorithms. These explore the most relevant chemistry spaces and suggest optimisation strategies that are most likely to succeed in your project.