Alzheimer’s disease is a neurodegenerative condition for which currently there are no drugs that can cure its devastating impact on human brain function. Although there are therapeutics that are being used in contemporary medicine for treatment against Alzheimer’s disease, new and more effective drugs are in great demand. In this work, we proposed three potential drug candidates which may act as multifunctional compounds simultaneously toward AChE, SERT, BACE1 and GSK3β protein targets. These candidates were discovered by using state-of-the-art methods as molecular calculations (molecular docking and molecular dynamics), artificial neural networks and multilinear regression models. These methods were used for virtual screening of the publicly available library containing more than twenty thousand compounds. The experimental testing enabled us to confirm a multitarget drug candidate active at low micromolar concentrations against two targets, e.g., AChE and BACE1.

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.