Prediction of organic homolytic bond dissociation enthalpies at near chemical accuracy with sub-second computational cost

Journal article

Bond dissociation enthalpies (BDEs) of organic molecules play a fundamental role in determining chemical reactivity and selectivity. However, BDE computations at sufficiently high levels of quantum mechanical theory require substantial computing resources. In this paper, we develop a machine learning model capable of accurately predicting BDEs for organic molecules in a fraction of a second. We perform automated density functional theory (DFT) calculations at the M06-2X/def2-TZVP level of theory for 42,577 small organic molecules, resulting in 290,664 BDEs. A graph neural network trained on a subset of these results achieves a mean absolute error of 0.58 kcal mol−1 (vs DFT) for BDEs of unseen molecules. We further demonstrate the model on two applications: first, we rapidly and accurately predict major sites of hydrogen abstraction in the metabolism of drug-like molecules, and second, we determine the dominant molecular fragmentation pathways during soot formation.

Authors: St John, PC; Guan, Y; Kim, Y; Kim, S; Paton, RS

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer Nature
• Journal: Nature Communications More from this journal
• Volume: 11
• Issue: 1
• Article number: 2328
• Place of publication: England
• Publication date: 2020-05-11
• Acceptance date: 2020-04-15
• DOI: 10.1038/s41467-020-16201-z
• EISSN: 2041-1723
• Pmid: 32393773
• Language: English
• Keywords: FFR