Virtual Lecture: Machine Learning for Experimental Synthetic Chemists

Lecture held on: July 28, 2021

Presenter

Prof. Abigail G. Doyle – Saul Winstein Chair of Organic Chemistry, Department of Chemistry and Biochemistry, University of California, Los Angeles, CA, USA

Numerous disciplines, such as image recognition and machine translation, have been revolutionized by
using machine learning (ML) to leverage big data. In organic synthesis, providing accurate chemical reactivity prediction with ML models could assist chemists with reaction prediction, optimization, and mechanistic interrogation. In this talk, Prof. Abigail G. Doyle covered her team’s efforts on experimental data collection and the quest to expand its availability and limit its bias for data science applications; feature engineering that may extend common intuition about the underlying chemistry; model assessments in the regime of small to medium size reaction datasets; and opportunities arising from accurate model predictions and their mechanistic interpretation.