2023
DOI: 10.21577/0100-4042.20230038
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MACHINE LEARNING-BASED VIRTUAL SCREENING, MOLECULAR DOCKING, DRUG-LIKENESS, PHARMACOKINETICS AND TOXICITY ANALYSES TO IDENTIFY NEW NATURAL INHIBITORS OF THE GLYCOPROTEIN SPIKE (S1) OF SARS-CoV-2

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 … Show more

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