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SAR study on inhibitors of Hsp90α using machine learning methods

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Abstract

Heat shock protein 90 (Hsp90) is a promising target for cancer treatment, developing new effective Hsp90 inhibitors is of great significance in anticancer therapy. In this study, 20 machine learning models were constructed on 1321 molecules in order to precisely classify highly active and weakly active Hsp90 inhibitors. Six types of fingerprints including MACCS keys (MACCS), Extended connectivity fingerprints with radius 2 (ECFP_4), PubChem fingerprints, Estate fingerprints, Substructure fingerprints and 2D atom pairs fingerprints were applied to characterize Hsp90 inhibitors. Five machine learning algorithms containing support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting decision tree (GBDT) and multilayer perceptron (MLP) were utilized to develop classification models. The best RF and SVM models resulted in MCC values of 0.8070 and 0.8003, respectively. The fingerprints of these best models were analyzed by information gain (IG) method. Based on the IG analysis, we found some favorable substructures of highly active Hsp90 inhibitors. Moreover, we clustered 1321 Hsp90 inhibitors into eight subsets, further analyzed and summarized the structural characteristics of each subset. It was found that purine scaffold and resorcinol appeared frequently in highly active Hsp90 inhibitors.

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Notes

  1. Reaxys: Elsevier Information Systems GmbH. https//www.reaxys.com/, Accessed October 20, 2020.

  2. SONNIA, version 4.2. Molecular Networks GmbH, Germany and Altamira, LLC, USA. http://www.molecular-networks.com.

  3. RDKit: Open-Source Cheminformatics Software. http://www.rdkit.org.

  4. Weka, version 3.8.1. https://www.cs.waikato.ac.nz/ml/weka/.

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Acknowledgements

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 programs CORINA Symphony and SONNIA for our scientific work.

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Authors and Affiliations

  1. State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, 15 BeiSanHuan East Road, P.O. Box 53, Beijing, 100029, People’s Republic of China

    Zhongyuan Zhang, Yujia Tian & Aixia Yan

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  1. Zhongyuan Zhang
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  2. Yujia Tian
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Correspondence to Aixia Yan.

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This paper is submitted for possible publication in the Special Issue on HPC in Life Science and Medical Science.

Supplementary Information

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All compounds used in this study can be found in txt files named Hsp90_database_1321.txt (TXT 107 KB)

Supplementary file2 (DOCX 21 KB)

42514_2021_84_MOESM3_ESM.docx

All optimum parameters of 20 classification models can be found in docx files named The optimum parameters.docx (DOCX 19 KB)

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Zhang, Z., Tian, Y. & Yan, A. SAR study on inhibitors of Hsp90α using machine learning methods. CCF Trans. HPC 3, 353–364 (2021). https://doi.org/10.1007/s42514-021-00084-7

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