ArticleMolecular diversity2013
Using self-organizing map (SOM) and support vector machine (SVM) for classification of selectivity of ACAT inhibitors.
Article in Molecular diversity, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Discovery of Cobimetinib as a novel A-FABP inhibitor using machine learning and molecular docking-based virtual screening.RSC advances · 2022Article
- Evaluation and Identification of the Neuroprotective Compounds of Xiaoxuming Decoction by Machine Learning: A Novel Mode to Explore the Combination Rules in Traditional Chinese Medicine Prescription.BioMed research international · 2019Article
- Screening of cytochrome P450 3A4 inhibitorsRSC advances · 2018Article
- In silico prediction of ROCK II inhibitors by different classification approaches.Molecular diversity · 2017Article
- Identification of potential ACAT-2 selective inhibitors using pharmacophore, SVM and SVR from Chinese herbs.Molecular diversity · 2016Article
- Consensus models for CDK5 inhibitors in silico and their application to inhibitor discovery.Molecular diversity · 2015Article
- Unsupervised genome-wide recognition of local relationship patterns.BMC genomics · 2013Article
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4 authors.
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Abstract
Using a self-organizing map (SOM) and support vector machine, two classification models were built to predict whether a compound is a selective inhibitor toward the two Acyl-coenzyme A: cholesterol acyltransferase (ACAT) isozymes, ACAT-1 and ACAT-2. A dataset of 97 ACAT inhibitors was collected. For each molecule, the global descriptors, 2D and 3D property autocorrelation descriptors and autocorrelation of surface properties were calculated from the program ADRIANA.Code. The prediction accuracies of the models (based on the training/ test set splitting by SOM method) for the test sets are 88.9 % for SOM1, 92.6 % for SVM1 model. In addition, the extended connectivity fingerprints (ECFP_4) for all the molecules were calculated and the structure-activity relationship of selective ACAT inhibitors was summarized, which may help find important structural features of inhibitors relating to the selectivity of ACAT isozymes.
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