To identify natural bioactive compounds (NBCs) as potential inhibitors of spike (S1) by means of in silico assays. NBCs with previously proven biological in vitro activity were obtained from the ZINC database and analyzed through virtual screening and molecular docking to identify those with higher affinity to the spike protein. Eight machine learning models were used to validate the results: Principal Component Analysis (PCA), Artificial Neural Network (ANN), Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Partial Least Squares-Discriminant Analysis (PLS-DA), Gradient Boosted Tree Discriminant Analysis (XGBoostDA), Soft Independent Modelling of Class Analogies (SIMCA) and Logistic Regression Discriminate Analysis (LREG). Selected NBCs were submitted to drug-likeness prediction using Lipinski’s and Veber’s rule of five. A prediction of pharmacokinetic parameters and toxicity was also performed (ADMET). Antivirals currently used for COVID-19 (remdesivir and molnupiravir) were used as a comparator. A total of 170,906 compounds were analyzed. Of these, 34 showed greater affinity with the S1 (affinity energy < -7 kcal mol-1). Most of these compounds belonged to the class of coumarins (benzopyrones), presenting a benzene ring fused to a lactone (group of heterosides). The PLS-DA model was able to reproduce the results of the virtual screening and molecular docking (accuracy of 97.0%). Of the 34 compounds, only NBC5 (feselol), NBC14, NBC15 and NBC27 had better results in ADMET predictions. These had similar binding affinity to S1 when compared to remdesivir and molnupirvir. Feselol and three other NBCs were the most promising candidates for treating COVID-19. In vitro and in vivo studies are needed to confirm these findings.

The amount and variety of new psychoactive substances (NPS) are expanding, and there are difficulties in assessing their risks. In this regard, in silico methods are potentially useful to predict NPS properties faster and at a lower cost. In this work a quantitative structure-activity relationship (QSAR) model was used to verify the risk of drugs derived from amphetamines and cathinones. A dataset of 26 derivatives with in vitro affinity for norepinephrine transporter (NET) was selected. To ensure reproducibility of the results, only geometric molecular descriptors (AM1 (Austin model 1) level) obtained from the platform ChemDes and ordered predictors selection (OPS) were used. The model presents good internal statistics (n = 23; coefficient of determination (R2) = 0.914). The small number of samples was divided into seven training sets (n = 17) and seven test sets (n = 6). The average R2pred = 0.754 showed that the model has good predictive capacity. Based on the tests, this model can accurately predict the risk range of three previously selected derivatives: methedrone (low), ethcathinone (medium), and methamphetamine (high), even when only data referring to NET are employed. We used these data to create a simple free program in Java that focuses on the risk assessment of recreational drugs belonging to this class of compounds.

Prostate cancer is one of the most common form of cancer among men, and its incidence has been increasing progressively. Since the prostate depends on androgenic hormones to regulate its growth and development, interference with androgenic biosynthesis are important to control the disease. Thus, in this work, a 4D-quantitative structure-activity relationship (QSAR) receptor independent study was carried out using the Laboratório de Quimiometria Teórica e Aplicada (LQTA)-QSAR approach and the new tool Web-4D-QSAR with a set of benzylidene oxazolidinedione and thiazolidinedione inhibitor of the 17β-hydroxysteroid dehydrogenase type 3. The obtained model is robust, free from chance correlation, and with good predictability in all tests performed. In addition to this result, a good mecanistic interpretation related to the binding with the biological target could be traced, which strengthens its potential application in virtual screening and design of new inhibitors of the studied enzyme with the possibility of use in the pharmacological treatment of prostate cancer.

In the present investigation, a collection of twenty two nerol derivatives, containing 1,2,3-triazolic appendages, was synthesized and screened in vitro for their cytotoxic activity against HL60, Nalm6, and Jurkat human leukemia cells as well as against B16F10 (melanoma cell line). In most cases, derivatives were able to reduce cell viability. The most potent compound (Z)-4-(((3,7-dimethylocta-2,6-dien-1-yl)oxy)methyl)-1-(4-(trifluoromethoxy)benzyl)-1H-1,2,3 triazole showed antiproliferative activity against Jurkat cells and reduced B16F10 cell migration. Physicochemical properties of the compounds were calculated in order to evaluate their potential for drug development. Most of the evaluated physicochemical parameters seemed to be favorable for drug development. In addition, for a better understanding of the biological activity results, 3D quantitative structure-activity relationship (QSAR) studies were carried out. 3D-QSAR studies indicate that the anticancer activities observed for the cell lines HL60 and Jurkat may occur by a similar mechanism of action and the same was found for the Nalm6 and B16F10 cell lines.