Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
This lecture discusses the progress towards leveraging machine learning for organic synthesis, focusing on predicting reaction yields using databases, high throughput experimentations, and the importance of dataset predictability. It also covers the NiCOlit dataset as a proof of concept, steps towards yield prediction, and predictive performances on random splitting. The comparison of NiCOlit and HTE datasets, including chemical space size, diversity, and scope/optimization structure, is analyzed. The lecture concludes with insights on the behavior of out-of-sample predictions, visualization of publications distribution in reaction space, and perspectives for predictive chemistry.