J Chem Inf Model - A Bayesian approach to in silico blood-brain barrier penetration modeling.

Tópicos

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Resumo

The human blood-brain barrier (BBB) is a membrane that protects the central nervous system (CNS) by restricting the passage of solutes. The development of any new drug must take into account its existence whether for designing new molecules that target components of the CNS or, on the other hand, to find new substances that should not penetrate the barrier. Several studies in the literature have attempted to predict BBB penetration, so far with limited success and few, if any, application to real world drug discovery and development programs. Part of the reason is due to the fact that only about 2% of small molecules can cross the BBB, and the available data sets are not representative of that reality, being generally biased with an over-representation of molecules that show an ability to permeate the BBB (BBB positives). To circumvent this limitation, the current study aims to devise and use a new approach based on Bayesian statistics, coupled with state-of-the-art machine learning methods to produce a robust model capable of being applied in real-world drug research scenarios. The data set used, gathered from the literature, totals 1970 curated molecules, one of the largest for similar studies. Random Forests and Support Vector Machines were tested in various configurations against several chemical descriptor set combinations. Models were tested in a 5-fold cross-validation process, and the best one tested over an independent validation set. The best fitted model produced an overall accuracy of 95%, with a mean square contingency coefficient () of 0.74, and showing an overall capacity for predicting BBB positives of 83% and 96% for determining BBB negatives. This model was adapted into a Web based tool made available for the whole community at http://b3pp.lasige.di.fc.ul.pt.

Resumo Limpo

human bloodbrain barrier bbb membran protect central nervous system cns restrict passag solut develop new drug must take account exist whether design new molecul target compon cns hand find new substanc penetr barrier sever studi literatur attempt predict bbb penetr far limit success applic real world drug discoveri develop program part reason due fact small molecul can cross bbb avail data set repres realiti general bias overrepresent molecul show abil permeat bbb bbb posit circumv limit current studi aim devis use new approach base bayesian statist coupl stateoftheart machin learn method produc robust model capabl appli realworld drug research scenario data set use gather literatur total curat molecul one largest similar studi random forest support vector machin test various configur sever chemic descriptor set combin model test fold crossvalid process best one test independ valid set best fit model produc overal accuraci mean squar conting coeffici show overal capac predict bbb posit determin bbb negat model adapt web base tool made avail whole communiti httpbpplasigedifculpt

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