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- W2906293588 abstract "Abstract GIIA secreted phospholipase A 2 (GIIA sPLA 2 ) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA 2 , we built 24 classification models by three machine learning algorithms including support vector machine (SVM), decision tree (DT), and random forest (RF) based on 452 compounds. The molecules were represented by CORINA descriptors, MACCS fingerprints, and ECFP4 fingerprints, respectively. The dataset was split into a training set containing 312 compounds and a test set containing 140 compounds by Kohonen's self‐organizing map (SOM) strategy and a random strategy. A recursive feature elimination (RFE) method and an information gain (IG) method were used in the selection of molecular descriptors. Three favorable performing models were obtained. They were built by SVM algorithm with CORINA descriptors (Models 1A and 2A) and ECFP4 fingerprints (Model 10A). In the prediction of test set of Model 10A, the accuracy reached 90.71%, and the Matthews correlation coefficient (MCC) values reached 0.82. In addition, the 452 inhibitors were clustered into eight subsets by K‐Means algorithm for analyzing their structural features. It was found that highly active inhibitors mainly contained indole scaffold or indolizine scaffold and four side chains." @default.
- W2906293588 created "2019-01-01" @default.
- W2906293588 creator A5003681868 @default.
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- W2906293588 creator A5043221366 @default.
- W2906293588 creator A5050408456 @default.
- W2906293588 creator A5064842058 @default.
- W2906293588 date "2019-02-04" @default.
- W2906293588 modified "2023-10-16" @default.
- W2906293588 title "SAR study on inhibitors of GIIA secreted phospholipase A<sub>2</sub>using machine learning methods" @default.
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- W2906293588 doi "https://doi.org/10.1111/cbdd.13470" @default.
- W2906293588 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30582300" @default.
- W2906293588 hasPublicationYear "2019" @default.
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