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SAR study on inhibitors of GIIA secreted phospholipase A2 using machine learning methods.

Abstract：

:GIIA secreted phospholipase A2 (GIIA sPLA2 ) is a potent target for drug discovery. To distinguish the activity level of the inhibitors of GIIA sPLA2 , 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.

journal_name

Chem Biol Drug Des

journal_title

Chemical biology & drug design

authors

Zhang S,Li Y,Qin Z,Tu G,Chen G,Yan A

doi

10.1111/cbdd.13470

subject

Has Abstract

pub_date

2019-05-01 00:00:00

pages

666-684

issue

5

eissn

1747-0277

issn

1747-0285

journal_volume

93

pub_type

杂志文章

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