Chemically Informed Deep Learning for Interpretable Radical Reaction Prediction.

Journal: Journal of chemical information and modeling
Published Date: Jan 28, 2025

Abstract

Organic radical reactions are crucial in many areas of chemistry, including synthetic, biological, and atmospheric chemistry. We develop a predictive framework based on the interaction of molecular orbitals that operates on mechanistic-level radical reactions. Given our chemistry-aware model, all predictions are provided with different levels of interpretability. Our models are trained and evaluated using the RMechDB database of radical reaction steps. Our model predicts the correct orbital interaction and products for 96% of the test reactions in RMechDB. By chaining these predictions, we perform a pathway search capable of identifying all intermediates and byproducts of a radical reaction. We test the pathway search on two classes of problems in atmospheric and polymerization chemistry. RMechRP is publicly available online at https://deeprxn.ics.uci.edu/rmechrp/.

Authors

  • Mohammadamin Tavakoli
    Department of Computer Science, University of California, Irvine, United States of America. Electronic address: mohamadt@uci.edu.
  • Yin Ting T Chiu
    Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States.
  • Ann Marie Carlton
    Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States.
  • David Van Vranken
    Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States.
  • Pierre Baldi
    Department of Computer Science, Department of Biological Chemistry, University of California-Irvine, Irvine, CA 92697, USA.

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