ArticleMolecular diversity2026
A predictive acetylcholinesterase inhibition model: an integrated computational approach on alkaloids and synthetic derivatives.
Article in Molecular diversity, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Computational techniques have become powerful tools for studying biological systems, including receptor-ligand (R-L) complexes. In medicinal chemistry, these in silico approaches are widely used for modeling and predicting molecular interactions, as well as for designing new ligands with biological activity. However, obtaining a direct correlation between the structure and activity of a set of active compounds is a challenging task. This study aims to develop a computational pipeline to find a direct correlation between structure and acetylcholinesterase (AChE) inhibitory activity across a structurally diverse set of 224 Amaryllidaceae alkaloids and synthetic derivatives. Standard docking protocols failed to generate reliable correlations with experimental data, and although the inclusion of molecular dynamics (MD) simulations improved performance, the results remained insufficient for robust prediction. Incorporation of quantum theory of atoms in molecules (QTAIM) analyses on MD-refined geometries was essential to capture key R-L interactions, yielding a strong correlation with relative IC
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