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Molecules 2018, 23(8), 1847; https://doi.org/10.3390/molecules23081847

Identification of Natural Compounds against Neurodegenerative Diseases Using In Silico Techniques

Institute of Chemistry, University of Tartu, Ravila 14a, 50411 Tartu, Estonia
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Received: 29 May 2018 / Revised: 14 July 2018 / Accepted: 21 July 2018 / Published: 25 July 2018
(This article belongs to the Special Issue Structure-Activity Relationship of Natural Products 2018)
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Abstract

The aim of this study was to identify new potentially active compounds for three protein targets, tropomyosin receptor kinase A (TrkA), N-methyl-d-aspartate (NMDA) receptor, and leucine-rich repeat kinase 2 (LRRK2), that are related to various neurodegenerative diseases such as Alzheimer’s, Parkinson’s, and neuropathic pain. We used a combination of machine learning methods including artificial neural networks and advanced multilinear techniques to develop quantitative structure–activity relationship (QSAR) models for all target proteins. The models were applied to screen more than 13,000 natural compounds from a public database to identify active molecules. The best candidate compounds were further confirmed by docking analysis and molecular dynamics simulations using the crystal structures of the proteins. Several compounds with novel scaffolds were predicted that could be used as the basis for development of novel drug inhibitors related to each target. View Full-Text
Keywords: natural compounds; artificial neural networks; molecular docking; TrkA; NMDA; LRRK2; molecular dynamics; CADD natural compounds; artificial neural networks; molecular docking; TrkA; NMDA; LRRK2; molecular dynamics; CADD
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Ivanova, L.; Karelson, M.; Dobchev, D.A. Identification of Natural Compounds against Neurodegenerative Diseases Using In Silico Techniques. Molecules 2018, 23, 1847.

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