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Accurate Enthalpies of Formation for Bioactive Compounds from High-Level Ab Initio Calculations with Detailed Conformational Treatment: a Case of Cannabinoids
Published
Author(s)
Andrei Kazakov, Eugene Paulechka
Abstract
Our recently developed approach based on local coupled-cluster with single, double, and perturbative triple excitation [LCCSD(T)] model gives very efficient means to compute the ideal-gas enthalpies of formation with an expanded uncertainty (95\% confidence) of about 3 kJ/mol for medium-sized compounds, comparable to typical experimental measurements. Larger compounds of interest often exhibit many conformations that can significantly differ in intramolecular interactions. Although the present capabilities allow processing even a few hundred distinct conformer structures for a given compound, many systems of interest exhibit numbers well in excess of 1,000. In this study, we investigate the possibility to reduce the number of expensive LCCSD(T) calculations for large conformer ensembles while controlling the error of the approximation. The best strategy found was to correct the results of the lower-level, surrogate model (density functional theory, DFT) in a systematic manner, thus significantly reducing the number of LCCSD(T) calculations. It was also found that the error in the conformational contribution introduced by a surrogate model is mainly driven by a systematic (bias) rather than a random component of the DFT energy deviation from the LCCSD(T) target. This distinction is usually overlooked in the DFT benchmarking studies. As a result of this work, the enthalpies of formation for 20 cannabinoid and cannabinoid-related compounds were obtained. Comprehensive uncertainty analysis suggests that the expanded uncertainties of the obtained values are well below 4 kJ/mol.
Kazakov, A.
and Paulechka, E.
(2025),
Accurate Enthalpies of Formation for Bioactive Compounds from High-Level Ab Initio Calculations with Detailed Conformational Treatment: a Case of Cannabinoids, Journal of Chemical Theory and Computation, [online], https://doi.org/10.1021/acs.jctc.4c01177, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=958515
(Accessed September 29, 2025)