ABSTRACT
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.
ABSTRACT
Introduction. We aimed to develop and validate machine learning (ML) -based algorithms to predict COVID-19 diagnosis as well as to identify new biomarkers associated with the disease. Methods. Initially, 96 blood samples of patients diagnosed with COVID-19 (Thaizhou Hospital, China) were analyzed through liquid chromatography coupled to mass spectrometry. Samples of patients presenting other pneumonias or severe acute respiratory syndrome, but with negative RT-PCR for SARS-CoV-2, were used as positive controls. Samples from healthy volunteers were used as negative controls. The final database included around 1000 metabolites. Exploratory analyses for the development of ML-based models using principal component analysis (PCA) were performed. Leverage plot versus studentized residuals method was used to detect outliers. Three supervised ML-based models were developed: discriminant analysis by partial least squares (PLS-DA), artificial neural networks discriminant analysis (ANNDA) and k-nearest neighbors (KNN). Samples for the training (70%) and testing sets (30%) were randomly selected using the Kenrad Stone algorithm. Models' performance was evaluated considering accuracy, sensitivity and specificity. Analyses were conducted in SOLO (Eigenvector-Research). Results. The PCA model was able to distinguish the three classes of patients' samples (positive for COVID-19, negative controls, positive controls) with an overall accumulated variance of 94.27 percent. The PLS-DA model presented the best performance (accuracy, sensitivity, and specificity of 93%, 98% and 88%, respectively). Increased levels of the biomarkers uridine (linked to glucose homeostasis, lipid, and amino acid metabolisms), 4-hydroxyphenylacetoylcarnitine (metabolite from the tyrosine metabolism;probably associated with anorexia) and ribothymidine (resulting from oral and fecal microbiota alterations) were significantly associated with COVID-19. Conclusions. Three different and updated ML-based algorithms were developed to predict COVID-19 diagnosis;PLS-DA led to the most accurate results. High levels of some metabolites were found as potentially predictors of the disease. These biomarkers should be further evaluated as potential therapeutic targets in well-designed clinical trials. These ML-based models can help the early diagnosis of COVID-19 and guide the development of tailored interventions.