GoodVibes is an open-source Python toolkit for processing the results of quantum chemical calculations. Thermochemical data are not simply parsed, but evaluated by evaluation of translational, rotational, vibrational and electronic partition functions. Changes in concentration, pressure, and temperature can be applied, and deficiencies in the rigid rotor harmonic oscillator treatment can be corrected. Vibrational scaling factors can also be applied by automatic detection of the level of theory and basis set. Absolute and relative thermochemical values are output to text and graphical plots in seconds. GoodVibes provides a transparent and reproducible way to process raw computational data into publication-quality tables and figures without the use of spreadsheets.

Acid catalysis has long been used to depolymerize plant cell wall polysaccharides, and the mechanisms by which acid affects carbohydrates have been extensively studied. Lignin depolymerization, however, is not as well understood, primarily due to the heterogeneity and reactivity of lignin. We present an experimental and theoretical study of acid-catalyzed cleavage of two non-phenolic and two phenolic dimers that exhibit the β-O-4 ether linkage, the most common intermonomer bond in lignin. This work demonstrates that the rate of acid-catalyzed β-O-4 cleavage in dimers exhibiting a phenolic hydroxyl group is 2 orders of magnitude faster than in non-phenolic dimers. The experiments suggest that the major product distribution is similar for all model compounds, but a stable phenyl-dihydrobenzofuran species is observed in the acidolysis of two of the γ-carbinol containing model compounds. The presence of a methoxy substituent, commonly found in native lignin, prevents the formation of this intermediate. Reaction pathways were examined with quantum mechanical calculations, which aid in explaining the substantial differences in reactivity. Moreover, we use a radical scavenger to show that the commonly proposed homolytic cleavage pathway of phenolic β-O-4 linkages is unlikely in acidolysis conditions. Overall, this study explains the disparity between rates of β-O-4 cleavage seen in model compound experiments and acid pretreatment of biomass, and implies that depolymerization of lignin during acid-catalyzed pretreatment or fractionation will proceed via a heterolytic, unzipping mechanism wherein β-O-4 linkages are cleaved from the phenolic ends of branched, polymer chains inward toward the core of the polymer.

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.

We have studied the cation–π interactions of neutral aromatic ligands with the cationic amino acid residues arginine, histidine and lysine using ab initio calculations, symmetry adapted perturbation theory (SAPT), and a systematic meta-analysis of all available Protein Data Bank (PDB) X-ray structures. Quantum chemical potential energy surfaces (PES) for these interactions were obtained at the DLPNO-CCSD(T) level of theory and compared against the empirical distribution of 2012 unique protein–ligand cation–π interactions found in X-ray crystal structures. We created a workflow to extract these structures from the PDB, filtering by interaction type and residue pK_a. The gas phase cation–π interaction of lysine is the strongest by more than 10 kcal mol⁻¹, but the empirical distribution of 582 X-ray structures lies away from the minimum on the interaction PES. In contrast, 1381 structures involving arginine match the underlying calculated PES with good agreement. SAPT analysis revealed that underlying differences in the balance of electrostatic and dispersion contributions are responsible for this behavior in the context of the protein environment. The lysine–arene interaction, dominated by electrostatics, is greatly weakened by a surrounding dielectric medium and causes it to become essentially negligible in strength and without a well-defined equilibrium separation. The arginine–arene interaction involves a near equal mix of dispersion and electrostatic attraction, which is weakened to a much smaller degree by the surrounding medium. Our results account for the paucity of cation–π interactions involving lysine, even though this is a more common residue than arginine. Aromatic ligands are most likely to interact with cationic arginine residues as this interaction is stronger than for lysine in higher polarity surroundings.