Progress towards leveraging Machine Learning for Organic Synthesis
Event details
| Date | 10.10.2023 |
| Hour | 16:00 › 17:00 |
| Speaker | Jules Schleinitz. Jules is currently a postdoctoral scholar at CalTech in the group of Sarah E. Reisman and a current member of the NSF Center for Computer Assisted Synthesis. His research focuses on the development of computational and machine learning tools for organic synthesis planning through mechanistic understanding. Jules graduated in 2022 from the Ecole Normale Supérieure in Paris. His PhD intitled “Machine learning and Mechanistic Analysis” was supervised by Laurence Grimaud. Alongside with his research activities, Jules spent half of his PhD teaching chemistry at Ecole Normale Supérieure (Organic chemistry: lessons, electrochemistry: tutorials and experimental sessions, experimental projects for bachelor and master students.). |
| Location | Online |
| Category | Conferences - Seminars |
| Event Language | English |
Predicting experimental organic synthesis outcomes is highly desirable for the development and testing of new drug candidates and the optimization of process chemistry. Machine learning offers great opportunities to tackle these challenging problems. However, due to the costs of experimentation and reaction product characterization, models trained on experimental results must perform in a low data regime. The discussion of reaction yield prediction on literature-extracted data focused on Nickel-Catalyzed C–O Couplings: NiCOlit, will show how the structure of published data impacts model performances. We will draw conclusions about how to design experimental datasets that are suited for modeling. Finally, we will discuss efforts to build surrogate models for the prediction of nitrogen-based ligands for Nickel catalysis and regioselectivity predictions based on the literature.
Practical information
- General public
- Free
Organizer
- Andres M Bran, Rebecca Neeser, Yannick Calvino, Philippe Schwaller
Contact
- Andres M Bran, Rebecca Neeser, Yannick Calvino, Philippe Schwaller