Tavakoli Mohammadamin, Chiu Yin Ting T, Carlton Ann Marie, Van Vranken David, Baldi Pierre
Department of Computer Science, University of California, Irvine, Irvine, California 92697, United States.
Department of Chemistry, University of California, Irvine, Irvine, California 92697, United States.
J Chem Inf Model. 2025 Feb 10;65(3):1228-1242. doi: 10.1021/acs.jcim.4c01901. Epub 2025 Jan 28.
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/.
有机自由基反应在化学的许多领域都至关重要，包括合成化学、生物化学和大气化学。我们基于分子轨道相互作用开发了一个预测框架，该框架适用于机理层面的自由基反应。基于我们的化学感知模型，所有预测都具有不同程度的可解释性。我们的模型使用自由基反应步骤的RMechDB数据库进行训练和评估。我们的模型对RMechDB中96%的测试反应预测出了正确的轨道相互作用和产物。通过链接这些预测，我们进行了一条路径搜索，能够识别自由基反应的所有中间体和副产物。我们在大气化学和聚合化学的两类问题上测试了路径搜索。RMechRP可在https://deeprxn.ics.uci.edu/rmechrp/ 在线公开获取。
