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

2020/05/11 by Peter C. St. John, Yanfei Guan, Yeonjoon Kim +2 · 1 citation
Chemistry · Computer Science · #Chemical Thermodynamics and Molecular Structure #Computational Drug Discovery Methods #Free Radicals and Antioxidants

paper · pdf · doi:10.1038/s41467-020-16201-z

openalex publication_date 2020/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/02

Abstract

Abstract 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.

Citations

• Improved sooting tendency measurements for aromatic hydrocarbons and their implications for naphthalene formation pathways
• A consistent and accurateab initioparametrization of density functional dispersion correction (DFT-D) for the 94 elements H-Pu
• Density-functional thermochemistry. III. The role of exact exchange
• Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94

Cited by

• Homolysis (chemistry) [wikipedia]