Please join Riley Jackson for his Molecular and Macromolecular Sciences Seminar Series presentation on " A Survey of Generative Machine Learning for Molecular Design and Discovery".

Abstract: In less than half a decade generative machine learning has rapidly become a critical tool for artificial creativity, leading to the construction of autonomous systems which generate artificial speech and images with eerily high fidelity. These successes have inspired computational chemists to apply these innovative algorithms to the difficult task of molecular discovery and design. Discovering a novel drug and taking it to market costs over $1 billion, and often takes well over 13 years. Because this process is so costly, reducing false leads is paramount. There is hope that generative machine learning can be used to generate high potential leads with low computational cost, decreasing the chance of false leads. In this seminar a handful of cutting-edge algorithms for molecular generation will be studied along with how molecules are typically represented to autonomous systems.

All are welcome to attend!!