Jackson Warner Burns
Kentucky native studying chemical engineering and computer science at the University of Delaware, Honors Class of '22. Interested in applications of computer science to traditional chemistry problems, such as the use of High Throughput Experimentation in chemical process design. Contributor to Open-Source community in both MATLAB and Python, as well as MATLAB Community.
Two-time hair donor, gradually approaching three.
Grab a copy of my CV here.
Get in touch! jburnsky |at| udel |dot| edu
Rapid development of pharmaceutical reagents is of the utmost interest for our collective health. Small molecule pharmaceuticals in particular are highly sought-after chemicals. Unfortunately the synthesis of these species can take months to discover and optimize because of a reliance on human-driven experimentation. Machine learning (ML) can be used to accelerate this process by building models to predict reaction conditions and yield and guide experimentation. Using a database of published chemical reactions, over 300 unique transformations involving samarium iodide from more than 200 separate publications are identified and used to build an accurate ML model. This approach could be used to automate chemical space exploration by allowing a computer to predict conditions for and then set up its own experiments, dramatically increasing the pace of discovery.