ArticleMolecular diversity2022
Improved machine learning scoring functions for identification of Electrophorus electricus's acetylcholinesterase inhibitors.
Article in Molecular diversity, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 14 citations in OpenAlex.
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- GAINSeq: glaucoma pre-symptomatic detection using machine learning models driven by next-generation sequencing data.Scientific reports · 2025Article
- Synthesis, Biological Evaluation, and Molecular Docking Studies of Novel Coumarin-Triazole-Isatin Hybrids as Selective Butyrylcholinesterase Inhibitors.Molecules (Basel, Switzerland) · 2025Article
- Chemical, In Cellulo, and In Silico Characterization of the Aminocholine Analogs of VG.International journal of molecular sciences · 2024Article
- Article
- Synthesis, biological evaluation, and computational studies ofJournal of enzyme inhibition and medicinal chemistry · 2023Article
- Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.Chemical research in toxicology · 2023Article
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9 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Structure-based drug design (SBDD) is an important in silico technique, used for the identification of enzyme inhibitors. Acetylcholinesterase (AChE), obtained from Electrophorus electricus (ee), is widely used for the screening of AChE inhibitors. It shares structural homology with the AChE of human and other organisms. Till date, the three-dimensional crystal structure of enzyme from ee is not available that makes it challenging to use the SBDD approach for the identification of inhibitors. A homology model was developed for eeAChE in the present study, followed by its structural refinement through energy minimisation. The docking protocol was developed using a grid dimension of 84 × 66 × 72 and grid point spacing of 0.375 Å for eeAChE. The protocol was validated by redocking a set of co-crystallised inhibitors obtained from mouse AChE, and their interaction profiles were compared. The results indicated a poor performance of the Autodock scoring function. Hence, a batch of machine learning-based scoring functions were developed. The validation results displayed an accuracy of 81.68 ± 1.73% and 82.92 ± 3.05% for binary and multiclass classification scoring function, respectively. The regression-based scoring function produced [Formula: see text] and [Formula: see text] values of 0.94, 0.635 and 0.634, respectively.
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