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Molecular Field Topology Analysis (MFTA) in the design of neuroprotective compoundsстатья
Информация о цитировании статьи получена из
Scopus
Дата последнего поиска статьи во внешних источниках: 26 февраля 2018 г.
- Авторы: Radchenko E.V., Palyulin V.A., Zefirov N.S.
- Сборник: Computational Modeling of Drugs Against Alzheimer’s Disease
- Серия: Neuromethods
- Том: 132
- Глава в коллективной монографии
- Год издания: 2018
- Место издания: Springer
- Первая страница: 139
- Последняя страница: 159
- DOI: 10.1007/978-1-4939-7404-7_5
- Аннотация: The Molecular Field Topology Analysis (MFTA) is a QSAR method designed to model the activities mediated by small molecule binding to biotargets using the local physicochemical descriptors reflecting the major types of ligand–target interactions. A molecular supergraph provides a common frame of reference for the meaningful comparison of the properties of atoms in different structures and the visualization of their effects. The MFTA method has been successfully used in the activity and selectivity modeling, design, and virtual screening of promising ligands of various enzymes and receptors (the NMDA and AMPA receptor antagonists and modulators as well as the acetyl- and butyrylcholinesterase inhibitors are of particular interest in the design of the anti-Alzheimer and other neuroprotective compounds). The design of potential anti-Alzheimer and other neuroprotective compounds based on the MFTA structure–activity models involves the following basic steps: (1) preparation of a training set containing structures of the compounds and the experimental activity and/or selectivity values, (2) generation of a series of MFTA models using various descriptor combinations, (3) evaluation of model quality and selection of the optimal model, (4) interpretation of the model, (5) preparation of the virtual screening library, (6) prediction of the activity and/or selectivity endpoints, (7) selection of promising compounds, and (8) prediction of the relevant ADMET properties. As a result, a focused library of potential neuroprotective compounds having the desired activity/selectivity profile and acceptable ADMET properties is obtained. The workflow presented in this chapter using a case study involving the inhibitors of glycogen synthase kinase 3β is applicable to many other relevant targets and ligand classes. In addition, some of its elements may be incorporated into other virtual screening and design workflows.
- Добавил в систему: Радченко Евгений Валерьевич