AI Chem. 2025, 1(1), 1; https://doi.org/10.3390/aichem1010001 - 8 Sep 2025
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Identifying the most reactive conformation of a molecule is a central challenge in computational chemistry, particularly when reactivity depends on subtle conformational effects. While most conformation search tools aim to find the lowest-energy structure, they often overlook the electronic descriptors that govern chemical
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Identifying the most reactive conformation of a molecule is a central challenge in computational chemistry, particularly when reactivity depends on subtle conformational effects. While most conformation search tools aim to find the lowest-energy structure, they often overlook the electronic descriptors that govern chemical reactivity. In this work, we present GAELLE, a cheminformatics tool that combines conformer generation with quantum reactivity descriptors to identify the most reactive structure of a molecule in solution. GAELLE integrates an evolutionary algorithm with fast semiempirical quantum chemical calculations (xTB), enabling the automated ranking of conformers based on HOMO–LUMO gap minimization (Pearson’s principle of maximum hardness) and electrophilicity index (Parr’s electrophilicity scale). Solvent effects are accounted for via implicit solvation models (GBSA/ALPB) to ensure realistic evaluation of reactivity in solution. The method is fully SMILES-driven, open-source, and scalable to medium-sized drug-like molecules. Applications to reactive intermediates, bioactive conformations, and pre-reactive complexes demonstrate the method’s relevance for mechanism elucidation, molecular design, and in silico screening. GAELLE is publicly available and offers a reactivity-focused alternative to traditional energy-minimization tools in conformational analysis.
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