Search

Constant research on natural products has generated, over time, a large number of compounds with the potential to be evaluated in several biological tests and subsequently have been cataloged in databases that allow other researchers to perform virtual screenings of activity in various biological systems. This considerably reduces the time for the development of new drugs. This review describes the main databases of natural products available for searching bioactive compounds. It also describes the main features of virtual screening strategies for the identification of molecules with the potential to be used as new drugs. In addition, a search was made in the Web of Science database, using the search term "Virtual screening of natural products databases" from 2003 to 2018. The search criterion resulted in 230 articles, which had their abstracts evaluated with pertinence to the criteria required for this work, which are: a) be a research article; b) performing a virtual screening on databases of natural products or containing natural products; and c) works that identified drug candidate molecules. Based on these criteria, the bibliographic review on the topic was excluded. After this analysis, 104 works were selected for this review. We selected relevant papers describing the potential drug candidates that were distributed in 15 classes, of which the anticancer, antibacterial and anti-inflammatory hits were the most abundant. The works showing efforts to search for new molecules against various other diseases in distinct biological systems were also described. In this way, this work shows an overview of several methodologies and we hope they can help and inspire the development of new research to improve people's quality of life.

Background: Staphylococcus aureus is a gram-positive spherical bacterium commonly present in nasal fossae and in the skin of healthy people; however, in high quantities, it can lead to complications that compromise health. The pathologies involved include simple infections, such as folliculitis, acne, and delay in the process of wound healing, as well as serious infections in the CNS, meninges, lung, heart, and other areas. Aim: This research aims to propose a series of molecules derived from 2-naphthoic acid as a bioactive in the fight against S. aureus bacteria through in silico studies using molecular modeling tools. Methods: A virtual screening of analogues was done in consideration of the results that showed activity according to the prediction model performed in the KNIME Analytics Platform 3.6, violations of the Lipinski rule, absorption rate, cytotoxicity risks, energy of binder-receptor interaction through molecular docking, and the stability of the best profile ligands in the active site of the proteins used (PDB ID 4DXD and 4WVG). Results: Seven of the 48 analogues analyzed showed promising results for bactericidal action against S. aureus. Conclusion: It is possible to conclude that ten of the 48 compounds derived from 2-naphthoic acid presented activity based on the prediction model generated, of which seven presented no toxicity and up to one violation to the Lipinski rule.

Aim: Selenium-based compounds have antitumor potential. We used a ligand-based virtual screening analysis to identify selenoglycolicamides with potential antitumor activity. Results & Conclusion: Compounds 3, 6, 7 and 8 were selected for in vitro cytotoxicity tests against various cell lines, according to spectrophotometry results. Compound 3 presented the best cytotoxicity results against a promyelocytic leukemia line (HL-60) and was able to induce cell death at a frequency similar to that observed for doxorubicin. The docking study showed that compound 3 has good interaction energies with the targets caspase-3, 7 and 8, which are components of the apoptotic pathway. These results suggested that selenium has significant pharmacological potential for the selective targeting of tumor cells, inducing molecular and cellular events that culminate in tumor cell death.

Infectious diseases are a health problem today and have high mortality rates with a wide diversity of potentially pathogenic microorganisms. Research that is based either on the search for new drugs from plants or on the improvement of phytotherapeutics is prominent and continues to play an important role nowadays. From this perspective, use of in silico studies to carry out investigations of new molecules potentially active for methicillin-resistant Staphylococcus aureus and Escherichia coli using an in-house database with 421 different secondary metabolites selected from the literature from Solanum genus was performed. We also realized an in vitro study with strains of S. aureus and E. coli and compared the results. Two databases from ChEMBL were selected, the first one with activity against methicillin-resistant S. aureus and another against E. coli. The compounds were classified according to the pIC50 values to generate and validate the model using a “Random Forest”. The “Random Forest” prediction model for methicillin-resistant S. aureus obtained an accuracy of 81%, area under the Receiver Operating Characteristic curve of 0.885, selecting eight molecules with an active potential above 60%. The prediction model for E. coli obtained an accuracy rate of 88%, area under the Receiver Operating Characteristic curve of 0.932, selecting four molecules with potential probability above 84%. Rutin proved to be potentially active in the in silico study for S. aureus and E. coli. Microbiological tests have shown that rutin has activity only for E. coli. An interaction study with strains of S. aureus ATCC 25923, a standard strain sensitive to all antibiotics, and SAM-01, a multidrug-resistant strain, was designed. There was interaction only between rutin and oxacillin, one of the three antibiotics studied in the interaction, for the strain SAM-01, reducing the resistance of this strain. Keywords: Rutin, Virtual Screening, Random Forest, Antibacterial activity

The objective of this work was to carry out a bibliographic survey of secondary metabolites isolated from the Velloziaceae family, creating a bank of compounds. After the bank was created, four prediction models for potentially active compounds against pathogenic microorganisms (Candida albicans, Escherichia coli, Pseudomonas aeruginosa and Salmonella sp.) were obtained trying to identify which metabolites would be more active against the strains. Four sets of compounds with known activity for microorganisms were selected for the construction of predictive models from the CHEMBL database. Another bank with 163 unique molecules isolated from the Velloziaceae family was built. The Volsurf+ v.1.0.7 software obtained the molecular descriptors and Knime 3.5 generated the in silico model. The performances of the internal and external tests were also analyzed. The study contributed through the virtual screening of a bank of metabolites to select several compounds with potential antimicrobial activity, highlighting the biflavonoid amentoflavone which showed potential activity against the four strains.

The microorganism Staphylococcus aureus is a gram-positive spherical bacteria present in the healthy human body, commonly found in the nostrils and skin. It is responsible for a number of infections such as inflammation in the follicles, contribution to the onset of acne, inflammation in the meninges, cardiovascular inflammation, lung inflammation and others. Benzoic compounds are widely used as drug prototypes for various diseases such as cancer, degenerative diseases, microbiological infections and inflammation, and viral infections. This work aims to propose new benzoic in silico bioactives to combat Staphylococcus aureus. To do this, a series of benzoic acid derivatives were submitted to a prediction model of biological activity developed in the KNIME Analytics Platform 3.6, where the molecules that showed activity in the model were imported into OSIRIS DataWarrior 4.7.3 to predict the risks of cytotoxicity, the calculation of the Lipinski rule violations and the absorption rate (% ABS). For the molecules approved by the virtual screening already commented, the calculation of the energies of interaction between receptor and binder by molecular docking and the analysis of the interactions with the residues of amino acids were carried out, comparing with the interactions of the drugs used as control in this research. As conclusion of this work it was possible to propose bioactive molecules against S. aureus based on the obtained computational data.

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.