Classification models and SAR analysis on thromboxane A₂ synthase inhibitors by machine learning methods

ABSTRACT

Thromboxane A₂ synthase (TXS) is a promising drug target for cardiovascular diseases and cancer. In this work, we conducted a structure-activity relationship (SAR) study on 526 TXS inhibitors for bioactivity prediction. Three types of descriptors (MACCS fingerprints, ECFP4 fingerprints, and MOE descriptors) were utilized to characterize inhibitors, 24 classification models were developed by support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), and deep neural networks (DNN). Then we reduced the number of fingerprints according to the contribution of descriptors to the models, and constructed 16 extra models on simplified fingerprints. In general, Model_4D built by DNN algorithm and 67 bits MACCS fingerprints performs best. The prediction accuracy of the model on the test set is 0.969, and Matthews correlation coefficient (MCC) is 0.936. The distance between compound and model (d_STD-PRO) was used to characterize the application domain of the model. In the test set of Model_4D, d_STD-PRO of 91.5% compounds is lower than the corresponding training set threshold (threshold_0.90 = 0.1055), and the accuracy of these compounds is 0.983. In addition, the important descriptors were summarized and further analyzed. It showed that aromatic nitrogenous heterocyclic groups were beneficial to improve the bioactivity of TXS inhibitors.

KEYWORDS:

• Thromboxane A₂ synthase (TXS) inhibitor
• structure-activity relationship (SAR)
• extreme gradient boosting (XGBoost)
• deep neural networks (DNN)
• applicability domain (AD)

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.2022.2078880

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. We thank the Molecular Networks GmbH, Nuremberg, Germany for providing the program SONNIA for our scientific work.