MACHINE LEARNING APPROACHES TO STUDY THE STRUCTURE-ACTIVITY RELATIONSHIPS OF LPXC INHIBITORS
Issued Date
2023-01-02
Resource Type
eISSN
16112156
Scopus ID
2-s2.0-85181568917
Journal Title
EXCLI Journal
Volume
22
Start Page
975
End Page
991
Rights Holder(s)
SCOPUS
Bibliographic Citation
EXCLI Journal Vol.22 (2023) , 975-991
Suggested Citation
Yu T., Chong L.C., Nantasenamat C., Anuwongcharoen N., Piacham T. MACHINE LEARNING APPROACHES TO STUDY THE STRUCTURE-ACTIVITY RELATIONSHIPS OF LPXC INHIBITORS. EXCLI Journal Vol.22 (2023) , 975-991. 991. doi:10.17179/excli2023-6356 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/95635
Title
MACHINE LEARNING APPROACHES TO STUDY THE STRUCTURE-ACTIVITY RELATIONSHIPS OF LPXC INHIBITORS
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Corresponding Author(s)
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Abstract
Antimicrobial resistance (AMR) has emerged as one of the global threats to human health in the 21st century. Drug discovery of inhibitors against novel targets rather than conventional bacterial targets has been considered an inevitable strategy for the growing threat of AMR infections. In this study, we applied quantitative structure-activity relationship (QSAR) modeling to the LpxC inhibitors to predict the inhibitory activity. In addition, we performed various cheminformatics analysis consisting of the exploration of the chemical space, identification of chemotypes, performing structure-activity landscape and activity cliffs as well as construction of the Structure-Activity Similarity (SAS) map. We built a total of 24 QSAR classification models using PubChem and MACCS fingerprint with 12 various machine learning algorithms. The best model with PubChem fingerprint is the Extremely Gradient Boost model (accuracy on the training set: 0.937; accuracy on the 10-fold cross-validation set: 0.795; accuracy on the test set: 0.799). Furthermore, it was found that the best model using the MACCS fingerprint was the Random Forest model (accuracy on the training set: 0.955; accuracy on the 10-fold cross-validation set: 0.803; accuracy on the test set: 0.785). In addition, we have identified eight consensus activity cliff generators that are highly informative for further SAR investigations. It is hoped that findings presented herein can provide guidance for further lead optimization of LpxC inhibitors.