J Chem Inf Model - A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition.

Tópicos

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Resumo

Prediction of CYP450 inhibition activity of small molecules poses an important task due to high risk of drug-drug interactions. CYP1A2 is an important member of CYP450 superfamily and accounts for 15% of total CYP450 presence in human liver. This article compares 80 in-silico QSAR models that were created by following the same procedure with different combinations of descriptors and machine learning methods. The training and test sets consist of 3745 and 3741 inhibitors and noninhibitors from PubChem BioAssay database. A heterogeneous external test set of 160 inhibitors was collected from literature. The studied descriptor sets involve E-state, Dragon and ISIDA SMF descriptors. Machine learning methods involve Associative Neural Networks (ASNN), K Nearest Neighbors (kNN), Random Tree (RT), C4.5 Tree (J48), and Support Vector Machines (SVM). The influence of descriptor selection on model accuracy was studied. The benefits of "bagging" modeling approach were shown. Applicability domain approach was successfully applied in this study and ways of increasing model accuracy through use of applicability domain measures were demonstrated as well as fragment-based model interpretation was performed. The most accurate models in this study achieved values of 83% and 68% correctly classified instances on the internal and external test sets, respectively. The applicability domain approach allowed increasing the prediction accuracy to 90% for 78% of the internal and 17% of the external test sets, respectively. The most accurate models are available online at http://ochem.eu/models/Q5747 .

Resumo Limpo

predict cyp inhibit activ small molecul pose import task due high risk drugdrug interact cypa import member cyp superfamili account total cyp presenc human liver articl compar insilico qsar model creat follow procedur differ combin descriptor machin learn method train test set consist inhibitor noninhibitor pubchem bioassay databas heterogen extern test set inhibitor collect literatur studi descriptor set involv estat dragon isida smf descriptor machin learn method involv associ neural network asnn k nearest neighbor knn random tree rt c tree j support vector machin svm influenc descriptor select model accuraci studi benefit bag model approach shown applic domain approach success appli studi way increas model accuraci use applic domain measur demonstr well fragmentbas model interpret perform accur model studi achiev valu correct classifi instanc intern extern test set respect applic domain approach allow increas predict accuraci intern extern test set respect accur model avail onlin httpochemeumodelsq

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