The glial cell line-derived neurotrophic factor (GDNF) family ligands (GFLs) support the survival and functioning of various neuronal populations. Thus, they could be attractive therapeutic agents against a multitude of neurodegenerative diseases caused by progressive death of GFLs responsive neurons. Small-molecule ligands BT13 and BT18 show an effect on GDNF family receptor GFRα1 and RET receptor tyrosine kinase RetA function. Thus, their potential binding sites and interactions were explored in the GDNF–GFRα1–RetA complex using molecular docking calculations as well as molecular dynamics (MD) simulations. Three possible regions were examined: the interface between GDNF and GFRα1 (region A), the RetA interface with GFRα1 (region B), and a possible allosteric site in GFRα1 (region C). The results obtained by the docking calculations and the MD simulations indicate that the preferable binding occurs at the allosteric site. A less preferable binding site was detected on the RetA surface interfacing GFRα1. In the membrane-bound state of RetA this can enable compounds BT13 and BT18 to act as direct RetA agonists. The analysis of the MD simulations shows hydrogen bonds for BT13 and significant hydrophobic interactions with GFRα1 for BT13 and BT18 at the allosteric site.

The modern development of computer technology and different in silico methods have had an increasing impact on the discovery and development of new drugs. Different molecular docking techniques most widely used in silico methods in drug discovery. Currently, the time and financial costs for the initial hit identification can be significantly reduced due to the ability to perform high-throughput virtual screening of large compound libraries in a short time. However, the selection of potential hit compounds still remains more of a random process, because there is still no consensus on what the binding energy and ligand efficiency (LE) of a potentially active compound should be. In the best cases, only 20–30% of compounds identified by molecular docking are active in biological tests. In this work, we evaluated the impact of the docking software used as well as the type of the target protein on the molecular docking results and their accuracy using an example of the three most popular programs and five target proteins related to neurodegenerative diseases. In addition, we attempted to determine the “reliable range” of the binding energy and LE that would allow selecting compounds with biological activity in the desired concentration range.

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.

To find out potential GDNF family receptor α1 (GFRα1) agonists, small molecules were built up by molecular fragments according to the structure-based drug design approach. Molecular docking was used to identify their binding modes to the biological target GFRα1 in GDNF-binding pocket. Thereafter, commercially available compounds based on the best predicted structures were searched from ZINC and MolPort databases (similarity ≥ 80%). Five compounds from the ZINC library were tested in phosphorylation and luciferase assays to study their ability to activate GFRα1–RET. A bidental compound with two carboxyl groups showed the highest activity in molecular modeling and biological studies. However, the relative position of these groups was important. The meta-substituted structure otherwise identical to the most active compound 2-[4-(5-carboxy-1H-1,3-benzodiazol-2-yl)phenyl]-1H-1,3-benzodiazole-5-carboxylic acid was inactive. A weaker activity was detected for a compound with a single carboxyl group, that is, 4-(1,3-benzoxazol-2-yl)benzoic acid. The substitution of the carboxyl group by the amino or acetamido group also led to the loss of the activity.

Background. Flaviviruses spread from endemic to non-endemic areas, causing illness in millions of people worldwide. The lack of effective therapies and the rapid expansion of flaviviral infections worldwide emphasize the importance of finding effective antivirals to treat such diseases. Objectives. To find out the potential broad-spectrum flavivirus inhibitors among previously reported inhibitors of DENV2/DENV4. Methods. The cytotoxicity of compounds was tested using WST-1 assay. The compounds were tested for their ability to inhibit the infection of DENV2, ZIKV, KUNV, and TBEV, and the most active compounds were also analyzed using the replicon-based assay. Interactions of one of the identified inhibitors with possible viral targets were studied using molecular dynamics simulations. Results. Two out of eight previously reported DENV2/DENV4 inhibitors demonstrated the ability to inhibit all studied viruses at low micromolar concentrations. Compound C6 demonstrated the ability to inhibit both DENV2 and TBEV. Compounds C1 (lycorine), C3 (mycophenolic acid), and C7 (vidarabine) were demonstrated as inhibitors of TBEV infection for the first time. Conclusions. Several compounds, previously described as inhibitors of DENV, are also able to inhibit other flaviviruses. This work is the first report on the anti-TBEV activity of lycorine (C1) and mycophenolic acid (C3), as well as vidarabine (C7). In addition, this is the first experimental confirmation of the antiviral activity of compound C5 and the lack of detectable antiviral activity of compound C8, demonstrating the necessity of experimental verification of the computational predictions.