Over ten years ago, research in our group was focused on the development of synthetic methods. Researchers would thus work on individual projects and would collaborate when synergy of goals or methods aligned. It was at this time we initiated projects aimed at scaling linear free energy relationships to use chemical descriptors derived from density functional theory calculations to correlate complex structural features of catalysts and/or substrates to measured reaction outcome (enantioselectivity, turnover frequency, yield, etc.). Our initial success in these projects, provided us a technique that we thought could be applied to modern problems in organic chemistry. Therefore, we sought to initiate numerous collaborations to test the techniques and inspire the development of new descriptors and data science workflows. These projects have spanned a wide range of problems in organic chemistry from asymmetric catalysis to organometallic chemistry to electrocatalysis to chemical biology. In all, we currently have active collaborations with &gt;20 unique collaborative teams. Most importantly, our group has adopted a culture of collaborative research and multi-disciplinary approaches to problem solving. This lecture will outline the evolution of our data science tools for organic chemistry highlighting how collaboration was the source of inspiration for diverse solutions to complex problems.

Applying data science tools to organic chemistry: A case study in collaborative research

Fall 2022 abstracts, posters, and presentations will launch in mid-September.

ACS Fall 2022

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.