Classification and QSAR models of leukotriene A4 hydrolase (LTA4H) inhibitors by machine learning methods

ABSTRACT

Leukotriene A4 hydrolase (LTA4H) is an important anti-inflammatory target which can convert leukotriene A4 (LTA4) into pro-inflammatory substance leukotriene B4 (LTB4). In this paper, we built 18 classification models for 463 LTA4H inhibitors by using support vector machine (SVM), random forest (RF) and K-Nearest Neighbour (KNN). The best classification model (Model 2A) was built from RF and MACCS fingerprints. The prediction accuracy of 88.96% and the Matthews correlation coefficient (MCC) of 0.74 had been achieved on the test set. We also divided the 463 LTA4H inhibitors into six subsets using K-Means. We found that the highly active LTA4H inhibitors mostly contained diphenylmethane or diphenyl ether as the scaffold and pyridine or piperidine as the side chain. In addition, six quantitative structure–activity relationship (QSAR) models for 172 LTA4H inhibitors were built by multiple linear regression (MLR) and SVM. The best QSAR model (Model 6A) was built by using SVM and CORINA Symphony descriptors. The coefficients of determination of the training set and the test set were equal to 0.81 and 0.79, respectively. Classification and QSAR models could be used for subsequent virtual screening, and the obtained fragments that were important for highly active inhibitors would be helpful for designing new LTA4H inhibitors.

KEYWORDS:

• Leukotriene A4 hydrolase (LTA4h) inhibitors
• quantitative structure- activity relationship (QSAR)
• support vector machine (SVM)
• random forest (RF)
• k-nearest neighbour (KNN)
• multiple linear regression (MLR)

Acknowledgements

We thank the Molecular Networks GmbH, Nuremberg, Germany for providing the programs SONNIA and CORINA Symphony for our scientific work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2021.1910862

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [21675010] and ‘Chemical Grid Project’ of Beijing University of Chemical Technology.