Ligand-Based Virtual Screening, Molecular Docking, and Molecular Dynamic Simulations of New ß-Estrogen Receptor Activators with Potential for Pharmacological Obesity Treatment.

Obesity is a pandemic and a serious health problem in developed and undeveloped countries. Activation of estrogen receptor beta (ERß) has been shown to promote weight loss without modifying caloric intake, making it an attractive target for developing new drugs against obesity. This work aimed to predict new small molecules as potential ERß activators. A ligand-based virtual screening of the ZINC15, PubChem, and Molport databases by substructure and similarity was carried out using the three-dimensional organization of known ligands as a reference. A molecular docking screening of FDA-approved drugs was also conducted as a repositioning strategy. Finally, selected compounds were evaluated by molecular dynamic simulations. Compounds 1 (-24.27 ± 0.34 kcal/mol), 2 (-23.33 ± 0.3 kcal/mol), and 6 (-29.55 ± 0.51 kcal/mol) showed the best stability on the active site in complex with ERß with an RMSD < 3.3 Å. RMSF analysis showed that these compounds do not affect the fluctuation of the Cα of ERß nor the compactness according to the radius of gyration. Finally, an in silico evaluation of ADMET showed they are safe molecules. These results suggest that new ERß ligands could be promising molecules for obesity control.

In silico design, synthesis and evaluation of a less toxic octinoxate alternative with suitable photoprotection properties.

Excessive UV exposure leads to several skin pathologies such as sunburns, photoaging and carcinogenesis. Currently, sunscreen use is the most important factor in protecting skin from photoinduced damage. Octinoxate is a commonly used UV filter, but its use has become controversial because it acts as an endocrine disruptor in both humans, and marine animals. Research has relied on biotechnology, structure activity relationship (SAR) studies and combinatorial chemistry to find new and less toxic UV filters. However, there are no current examples that describe the possible applications of in silico techniques for obtaining these compounds. Thus, this project sought to design an octinoxate analog that could be used as a less toxic, but equally effective, photoprotective alternative through ligand based virtual screening (LBVS). We designed 213 novel molecules based on the (E)-cinnamoyl moiety of octinoxate, but only 23 were found to be less toxic than the parent compound. Then, an artificial neural network (ANN) based model was built to predict the molar absorptivity of those 23 molecules, and the molecule that presented a similar molar absorptivity to that of octinoxate was chosen for synthesis (analog 4, 3-phenylpropyl (E)-3-(4-methoxyphenyl)acrylate). Synthesis for analog 4 resulted in a 90% yield, and its photoprotective properties, lipophilicity and cytotoxicity were then evaluated. Analog 4 absorbed UV radiation in the range of 250-340 nm, and it presented a molar absorptivity of 36,155 M - 1cm-1. Its lipophilicity was evaluated with RP-HPLC resulting in a logkw of 2.49 and its LC50 was greater than octinoxate's (67.41 nM vs. 45.67 nM). Therefore, results showed that ligand based virtual screening is an effective strategy for the development of new organic UV filters, because it guided the design of less toxic analogs and pinpointed the most likely analog to exhibit UV properties similar to those of octinoxate. In this case, analog 4 is a promising alternative to its parent compound since it proved to be more effective and less toxic.

Review on Compounds Isolated from Eriocaulaceae Family and Evaluation of Biological Activities by Machine Learning.

Eriocaulaceae is a pantropical family whose main center of biodiversity is in Brazil. In general, the family has about 1200 species, in which phytochemical and biological studies have shown a variety of structures and activities. The aim of this research is to compile the compounds isolated in the Eriocaulaceae family and carry out a computational study on their biological targets. The bibliographic research was carried out on six databases. Tables were built and organized according to the chemical class. In addition, a summary of the methods of isolating the compounds was also made. In the computational study were used ChEMBL platform, DRAGON 7.0, and the KNIME 4.4.0 software. Two hundred and twenty-two different compounds have been isolated in sixty-eight species, divided mainly into flavonoids and naphthopyranones, and minor compounds. The ligand-based virtual screening found promising molecules and molecules with multitarget potential, such as xanthones 194, 196, 200 and saponin 202, with xanthone 194 as the most promising. Several compounds with biological activities were isolated in the family, but the chemical profiles of many species are still unknown. The selected structures are a starting point for further studies to develop new antiparasitic and antiviral compounds based on natural products.

Natural Products from Annonaceae as Potential Antichagasic Agents.

Chagas disease, a neglected tropical disease, is endemic in 21 Latin American countries and particularly prevalent in Brazil. Chagas disease has drawn more attention in recent years due to its expansion into non-endemic areas. The aim of this work was to computationally identify and experimentally validate the natural products from an Annonaceae family as antichagasic agents. Through the ligand-based virtual screening, we identified 57 molecules with potential activity against the epimastigote form of T. cruzi. Then, 16 molecules were analyzed in the in vitro study, of which, six molecules displayed previously unknown antiepimastigote activity. We also evaluated these six molecules for trypanocidal activity. We observed that all six molecules have potential activity against the amastigote form, but no molecules were active against the trypomastigote form. 13-Epicupressic acid seems to be the most promising, as it was predicted as an active compound in the in silico study against the amastigote form of T. cruzi, in addition to having in vitro activity against the epimastigote form.

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the ester bonds in acetylcholine and plays an important role in the termination of acetylcholine activity at cholinergic synapses in various regions of the nervous system. The inhibition of acetylcholinesterase is frequently used to treat Alzheimer's disease. In this study, a merged BindingDB and ChEMBL dataset containing molecules with reported half-maximal inhibitory concentration (IC50) values for AChE (7032 molecules) was used to build machine learning classification models for selecting potential AChE inhibitors from the SistematX dataset (8593 secondary metabolites). A total of seven fivefold models with accuracy above 80% after cross-validation were obtained using three types of molecular descriptors (VolSurf, DRAGON 5.0, and bit-based fingerprints). A total of 521 secondary metabolites (6.1%) were classified as active in this stage. Subsequently, virtual screening was performed, and 25 secondary metabolites were identified as potential inhibitors of AChE. Separately, the crystal structure of AChE in complex with (-)-galantamine was used to perform molecular docking calculations with the entire SistematX dataset. Consensus analysis of both methodologies was performed. Only eight structures achieved combined probability values above 0.5. Finally, two sesquiterpene lactones, structures 15 and 24, were predicted to be able to cross the blood-brain barrier, which was confirmed in the VolSurf+ quantitative model, revealing these two structures as the most promising secondary metabolites for AChE inhibition among the 8593 molecules tested. A consensus analysis of classification models and molecular docking calculations identified four potential inhibitors of acetylcholinesterase from the SistematX dataset (8593 structures).

Selection of antileishmanial sesquiterpene lactones from SistematX database using a combined ligand-/structure-based virtual screening approach.

Leishmaniasis refers to a complex of diseases, caused by the intracellular parasitic protozoans belonging to the genus Leishmania. Among the three types of disease manifestations, the most severe type is visceral leishmaniasis, which is caused by Leishmania donovani, and is diagnosed in more than 20,000 cases annually, worldwide. Because the current therapeutic options for disease treatment are associated with several limitations, the identification of new potential leads/drugs remains necessary. In this study, a combined approach was used, based on two different virtual screening (VS) methods, which were designed to select promising antileishmanial agents from among the entire sesquiterpene lactone (SL) dataset registered in SistematX, a web interface for managing a secondary metabolite database that is accessible by multiple platforms on the Internet. Thus, a ChEMBL dataset, including 3159 and 1569 structures that were previously tested against L. donovani amastigotes and promastigotes in vitro, respectively, was used to develop two random forest models, which performed with greater than 74% accuracy in both the cross-validation and test sets. Subsequently, a ligand-based VS assay was performed against the 1306 SistematX-registered SLs. In parallel, the crystal structures of three L. donovani target proteins, N-myristoyltransferase, ornithine decarboxylase, and mitogen-activated protein kinase 3, and a homology model of pteridine reductase 1 were used to perform a structure-based VS, using molecular docking, of the entire SistematX SL dataset. The consensus analysis of these two VS approaches resulted in the normalization of probability scores and identified 13 promising, enzyme-targeting, antileishmanial SLs from SistematX that may act against L. donovani. A combined approach based on two different virtual screening methods (structure-based and ligand-based) was performed using an in-house dataset composed of 1306 sesquiterpene lactones to identify potential antileishmanial (Leishmania donovani) structures.

Ligand-Based Virtual Screening, Molecular Docking, Molecular Dynamics, and MM-PBSA Calculations towards the Identification of Potential Novel Ricin Inhibitors.

Ricin is a toxin found in the castor seeds and listed as a chemical weapon by the Chemical Weapons Convention (CWC) due to its high toxicity combined with the easiness of obtention and lack of available antidotes. The relatively frequent episodes of usage or attempting to use ricin in terrorist attacks reinforce the urge to develop an antidote for this toxin. In this sense, we selected in this work the current RTA (ricin catalytic subunit) inhibitor with the best experimental performance, as a reference molecule for virtual screening in the PubChem database. The selected molecules were then evaluated through docking studies, followed by drug-likeness investigation, molecular dynamics simulations and Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) calculations. In every step, the selection of molecules was mainly based on their ability to occupy both the active and secondary sites of RTA, which are located right next to each other, but are not simultaneously occupied by the current RTA inhibitors. Results show that the three PubChem compounds 18309602, 18498053, and 136023163 presented better overall results than the reference molecule itself, showing up as new hits for the RTA inhibition, and encouraging further experimental evaluation.

Virtual Screening Techniques in Drug Discovery: Review and Recent Applications.

The discovery of bioactive molecules is an expensive and time-consuming process and new strategies are continuously searched for in order to optimize this process. Virtual Screening (VS) is one of the recent strategies that has been explored for the identification of candidate bioactive molecules. The number of new techniques and software that can be applied in this strategy has grown considerably in recent years, so, before their use, it is necessary to understand the basics an also the limitations behind each one to get the most out of them. It is also necessary to assess the real contributions of this strategy so that more significant progress can be made in the future. In this context, this review aims to discuss some important points related to VS, including the use of virtual ligand and biotarget libraries, structurebased and ligand-based VS techniques, as well as to present recent cases where this strategy was successfully applied.

Exploring Secondary Metabolites Database of Apocynaceae, Menispermaceae, and Annonaceae to Select Potential Anti-HCV Compounds.

BACKGROUND: Hepatitis C is a disease that constitutes a serious global health problem, is often asymptomatic and difficult to diagnose and about 60-80% of infected patients develop chronic diseases over time. As there is no vaccine against hepatitis C virus (HCV), developing new cheap treatments is a big challenge. OBJECTIVE: The search for new drugs from natural products has been outstanding in recent years. The aim of this study was to combine structure-based and ligand-based virtual screening (VS) techniques to select potentially active molecules against four HCV target proteins from in-house secondary metabolite dataset (SistematX). MATERIALS AND METHODS: From the ChEMBL database, we selected four sets of 1199, 355, 290 and 237chemical structures with inhibitory activity against different targets of HCV to create random forest models with an accuracy value higher than 82% for cross-validation and test sets. Afterward, a ligandbased virtual screen of the entire 1848 secondary metabolites database stored in SistematX was performed. In addition, a structure-based virtual screening was also performed for the same set of secondary metabolites using molecular docking. RESULTS: Finally, using consensus analyses approach combining ligand-based and structure-based VS, three alkaloids were selected as potential anti-HCV compounds. CONCLUSION: The selected structures are a starting point for further studies in order to develop new anti- HCV compounds based on natural products.

Targeting the Plasmodium falciparum plasmepsin V by ligand-based virtual screening.

Malaria is a devastating disease depending only on chemotherapy as treatment. However, medication is losing efficacy, and therefore, there is an urgent need for the discovery of novel pharmaceutics. Recently, plasmepsin V, an aspartic protease anchored in the endoplasmaic reticulum, was demonstrated as responsible for the trafficking of parasite-derived proteins to the erythrocytic surface and further validated as a drug target. In this sense, ligand-based virtual screening has been applied to design inhibitors that target plasmepsin V of P. falciparum (PMV). After screening 5.5 million compounds, four novel plasmepsin inhibitors have been identified which were subsequently analyzed for the potency at the cellular level. Since PMV is membrane-anchored, the verification in vivo by using transgenic PMV overexpressing P. falciparum cells has been performed in order to evaluate drug efficacy. Two lead compounds, revealing IC50 values were 44.2 and 19.1 µm, have been identified targeting plasmepsin V in vivo and do not significantly affect the cell viability of human cells up to 300 µm. We herein report the use of the consensus of individual virtual screening as a new technique to design new ligands, and we propose two new lead compounds as novel protease inhibitors to target malaria.