UNDERSTANDING THE ROLE OF AROMATICITY IN TAUTOMERIC EQUILIBRIA
- 1Department of Chemistry, Johannes Gutenberg University Mainz, Duesbergweg 10–14, Mainz, Germany, 55128
- 2Institute for Quantitative and Computational Biosciences (IQCB, Johannes Gutenberg University Mainz, Johannes-von-Müller-Weg 6, Mainz, Germany, 55128
Abstract
Tautomerism, the interconversion of small molecules via proton and π-electron shifts, poses a fundamental challenge in drug discovery, as different tautomers can exhibit distinct physicochemical properties and receptor-binding profiles, while computational approaches have been widely applied to predict tautomeric equilibria, most methods often overlook electronic properties such as aromaticity, which are critical to molecular stability. Here, we explore the role of aromaticity in tautomeric equilibria using a computational framework that integrates density functional theory (DFT) calculations with interpretable machine learning (ML) models. Aromaticity, which is challenging to measure experimentally, is quantified via Harmonics Oscillator Model of Aromaticity (HOMA) values and incorporated as a molecular descriptor. To enhance interpretability, we employ shallow ML models and assess feature contributions both intrinsically and using post-hoc explainability techniques. Our results demonstrate that both intrinsic and post hoc feature importance approaches capture similar trends associated with aromaticity. This highlights the complex influence of aromaticity on tautomeric equilibria and suggest that ML models, when combined with quantum-mechanics-based descriptors and explainable feature analyses, could be applied more broadly to capture important trends in other physicochemical properties beyond tautomer stability.
Related
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BibTeX
@misc{lim2026understanding,
title = {{UNDERSTANDING THE ROLE OF AROMATICITY IN TAUTOMERIC EQUILIBRIA}},
author = {Michel Lim and Paul Czodrowski and Oscar Palomino-Hernandez},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010863/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010863/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - UNDERSTANDING THE ROLE OF AROMATICITY IN TAUTOMERIC EQUILIBRIA
AU - Michel Lim
AU - Paul Czodrowski
AU - Oscar Palomino-Hernandez
PY - 2026
DA - 2026/10/22
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010863/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010863/v1
AB - Tautomerism, the interconversion of small molecules via proton and π-electron shifts, poses a fundamental challenge in drug discovery, as different tautomers can exhibit distinct physicochemical properties and receptor-binding profiles, while computational approaches have been widely applied to predict tautomeric equilibria, most methods often overlook electronic properties such as aromaticity, which are critical to molecular stability. Here, we explore the role of aromaticity in tautomeric equilibria using a computational framework that integrates density functional theory (DFT) calculations with interpretable machine learning (ML) models. Aromaticity, which is challenging to measure experimentally, is quantified via Harmonics Oscillator Model of Aromaticity (HOMA) values and incorporated as a molecular descriptor. To enhance interpretability, we employ shallow ML models and assess feature contributions both intrinsically and using post-hoc explainability techniques. Our results demonstrate that both intrinsic and post hoc feature importance approaches capture similar trends associated with aromaticity. This highlights the complex influence of aromaticity on tautomeric equilibria and suggest that ML models, when combined with quantum-mechanics-based descriptors and explainable feature analyses, could be applied more broadly to capture important trends in other physicochemical properties beyond tautomer stability.
ER -