J Chem Inf Model - Predictions of BuChE inhibitors using support vector machine and naive Bayesian classification techniques in drug discovery.

Tópicos

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Resumo

Butyrylcholinesterase (BuChE, EC 3.1.1.8) is an important pharmacological target for Alzheimer's disease (AD) treatment. However, the currently available BuChE inhibitor screening assays are expensive, labor-intensive, and compound-dependent. It is necessary to develop robust in silico methods to predict the activities of BuChE inhibitors for the lead identification. In this investigation, support vector machine (SVM) models and naive Bayesian models were built to discriminate BuChE inhibitors (BuChEIs) from the noninhibitors. Each molecule was initially represented in 1870 structural descriptors (1235 from ADRIANA.Code, 334 from MOE, and 301 from Discovery studio). Correlation analysis and stepwise variable selection method were applied to figure out activity-related descriptors for prediction models. Additionally, structural fingerprint descriptors were added to improve the predictive ability of models, which were measured by cross-validation, a test set validation with 1001 compounds and an external test set validation with 317 diverse chemicals. The best two models gave Matthews correlation coefficient of 0.9551 and 0.9550 for the test set and 0.9132 and 0.9221 for the external test set. To demonstrate the practical applicability of the models in virtual screening, we screened an in-house data set with 3601 compounds, and 30 compounds were selected for further bioactivity assay. The assay results showed that 10 out of 30 compounds exerted significant BuChE inhibitory activities with IC50 values ranging from 0.32 to 22.22 ?M, at which three new scaffolds as BuChE inhibitors were identified for the first time. To our best knowledge, this is the first report on BuChE inhibitors using machine learning approaches. The models generated from SVM and naive Bayesian approaches successfully predicted BuChE inhibitors. The study proved the feasibility of a new method for predicting bioactivities of ligands and discovering novel lead compounds.

Resumo Limpo

butyrylcholinesteras buch ec import pharmacolog target alzheim diseas ad treatment howev current avail buch inhibitor screen assay expens laborintens compounddepend necessari develop robust silico method predict activ buch inhibitor lead identif investig support vector machin svm model naiv bayesian model built discrimin buch inhibitor buchei noninhibitor molecul initi repres structur descriptor adrianacod moe discoveri studio correl analysi stepwis variabl select method appli figur activityrel descriptor predict model addit structur fingerprint descriptor ad improv predict abil model measur crossvalid test set valid compound extern test set valid divers chemic best two model gave matthew correl coeffici test set extern test set demonstr practic applic model virtual screen screen inhous data set compound compound select bioactiv assay assay result show compound exert signific buch inhibitori activ ic valu rang m three new scaffold buch inhibitor identifi first time best knowledg first report buch inhibitor use machin learn approach model generat svm naiv bayesian approach success predict buch inhibitor studi prove feasibl new method predict bioactiv ligand discov novel lead compound

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