Molecular Docking Guided Comparative GFA, G/PLS, SVM and ANN Models of Structurally Diverse Dual Binding Site Acetylcholinesterase Inhibitors
| Autor: | Päivi Järvinen, Shikhar Gupta, Pia Vuorela, Adyary Fallarero, Mark S. Johnson, C. Gopi Mohan, Mikko J. Vainio, J. Santeri Puranen, Parameswaran Saravanan |
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| Rok vydání: | 2011 |
| Předmět: |
Quantitative structure–activity relationship
Computational biology Machine learning computer.software_genre 01 natural sciences Linear methods 03 medical and health sciences chemistry.chemical_compound Structural Biology Drug Discovery Partial least squares regression Binding site 030304 developmental biology 0303 health sciences Artificial neural network Chemistry business.industry Organic Chemistry Acetylcholinesterase 0104 chemical sciences Computer Science Applications Support vector machine 010404 medicinal & biomolecular chemistry Docking (molecular) Molecular Medicine Artificial intelligence business computer |
| Zdroj: | Molecular informatics. 30(8) |
| ISSN: | 1868-1743 |
| Popis: | Recently discovered 42 AChE inhibitors binding at the catalytic and peripheral anionic site were identified on the basis of molecular docking approach, and its comparative quantitative structure-activity relationship (QSAR) models were developed. These structurally diverse inhibitors were obtained by our previously reported high-throughput in vitro screening technique using 384-well plate's assay based on colorimetric method of Ellman. QSAR models were developed using (i) genetic function algorithm, (ii) genetic partial least squares, (iii) support vector machine and (iv) artificial neural network techniques. The QSAR model robustness and significance was critically assessed using different cross-validation techniques on test data set. The generated QSAR models using thermodynamic, electrotopological and electronic descriptors showed that nonlinear methods are more robust than linear methods, and provide insight into the structural features of compounds that are important for AChE inhibition. |
| Databáze: | OpenAIRE |
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