MUBD-DecoyMaker2.0 and MUBD methodology

Introduction

MUBD-DecoyMaker2.0, an updated version of MUBD-DECOYMAKER, is a Python GUI application (standalone version) to generate maximal unbiased benchmarking sets data sets for virtual drug screening. It can be run on Windows-based machine and has no dependency.

Availability & Implementation

• Due to the large size, the Python GUI application has been uploaded to the cloud drives and is accessible from there： https://www.dropbox.com/s/dfa35t2jb0fnarx/MUBD-DecoyMaker2.0.exe?dl=0 (Outside China) or https://pan.baidu.com/s/110FJY6YHxcaUmI7fSyUSkw (China)

Documentation

• The manual for the Python GUI application and the files for the case study are available at this website, i.e. 'manual.for.MUBD-Decoymaker2.0.pdf' and 'Case-ACM4-Agonists.zip'.

Installation

• We recommend the users to run the Python GUI application on Windows-based machines, as all the dependencies have been included. Please note we used the machine with Intel Core(TM) i7-7700 CPU@3.60GHz and RAM of 16 GB for testing the tool. The computation time for the test case of ACM Agonists was 1882 seconds.

References

Computational tool, Methodology and Datasets:

1. Xia, J., Li, S., Ding, Y., Wu, S. and Wang, X.S., MUBD‐DecoyMaker 2.0: A Python GUI Application to Generate Maximal Unbiased Benchmarking Data Sets for Virtual Drug Screening. Mol. Inf.,2020, 39, 1900151. https://doi.org/10.1002/minf.201900151 (Tool)
2. Xia, J.; Jin, H.; Liu, Z.; Zhang, L.; Wang, X.S., An unbiased method to build benchmarking sets for ligand-based virtual screening and its application to GPCRs. J. Chem. Inf. Model., 2014, 54 (5): 1433-1450. https://pubs.acs.org/doi/10.1021/ci500062f (Algorithm and MUBD-GPCRs/ULS/UDS)
3. Xia, J.; Tilahun, E.L.; Kebede, E.H.; Reid, T.E.; Zhang, L.;Wang, X.S.,Comparative modeling and benchmarking data sets for human histone deacetylases and sirtuin families. J. Chem. Inf. Model., 2015, 55 (2): 374-388. https://pubs.acs.org/doi/10.1021/ci5005515 (MUBD-HDACs)
4. Xia, J.; Reid, T.E.; Wu, S.; Zhang, L.; Wang, X.S., Maximal Unbiased Benchmarking Data Sets for Human Chemokine Receptors and Comparative Analysis. J. Chem. Inf. Model., 2018, 58 (5): 1104-1120. https://pubs.acs.org/doi/10.1021/acs.jcim.8b00004 (MUBD-hCRs)
5. Xia, J.; Tilahun, E. L.; Reid, T. E.; Zhang, L.; Wang, X. S., Benchmarking methods and data sets for ligand enrichment assessment in virtual screening. Methods 2015, 71, 146-57. https://doi.org/10.1016/j.ymeth.2014.11.015 (PseudoCode)

Selected Applications of MUBD methodology

1. Shen, Wf., Tang, Hw., Li, Jb. et al. Multimodal data fusion for supervised learning-based identification of USP7 inhibitors: a systematic comparison. J Cheminform, 2023, 15, 5. https://doi.org/10.1186/s13321-022-00675-8
2. Huo, D., Sun, Z., Wang, M., & Yan, A. Ligand and structure based hierarchical virtual screening cascade for finding novel epidermal growth factor receptor inhibitors. Chemical Biology & Drug Design,2023, 00, 1–17. https://doi.org/10.1111/cbdd.14375
3. Pang, X.; Zhao, Y.; Li, G.; Liu, J.; Yan, A., SAR and QASR study on Cyclin dependent kinase 4 inhibitors using machine learning methods. Digital Discovery 2023. https://doi.org/10.1039/D2DD00143H
4. Djokovic, N.; Ruzic, D.; Rahnasto-Rilla, M.; Srdic-Rajic, T.; Lahtela-Kakkonen, M.; Nikolic, K., Expanding the Accessible Chemical Space of SIRT2 Inhibitors through Exploration of Binding Pocket Dynamics. J. Chem. Inf. Model. 2022, 62 (10), 2571-2585. https://doi.org/10.1021/acs.jcim.2c00241
5. Li, N.; Yin, L.; Chen, X.; Shang, J.; Liang, M.; Gao, L.; Qiang, G.; Xia, J.; Du, G.; Yang, X. Combination of Docking-Based and Pharmacophore-Based Virtual Screening Identifies Novel Agonists That Target the Urotensin Receptor. Molecules 2022, 27, 8692. https://doi.org/10.3390/molecules27248692
6. Dou, X.; Sun, X.; Huang, H.; Jiang, L.; Jin, Z.; Liu, Y.; Zou, Y.; Li, Z.; Zhu, G.; Jin, H.; Jiao, N.; Zhang, L.; Liu, Z.; Zhang, L., Discovery of novel ataxia telangiectasia mutated (ATM) kinase modulators: Computational simulation, biological evaluation and cancer combinational chemotherapy study. Eur. J. Med. Chem. 2022, 114196. https://doi.org/10.1016/j.ejmech.2022.114196
7. Huo, D.; Wang, S.; Kong, Y.; Qin, Z.; Yan, A., Discovery of Novel Epidermal Growth Factor Receptor (EGFR) Inhibitors Using Computational Approaches. J. Chem. Inf. Model. 2022, 62, 21, 5149–5164 https://doi.org/10.1021/acs.jcim.1c00884
8. Sedykh, A. Y.; Shah, R. R.; Kleinstreuer, N. C.; Auerbach, S. S.; Gombar, V. K., Saagar–A New, Extensible Set of Molecular Substructures for QSAR/QSPR and Read-Across Predictions. Chem. Res. Toxicol. 2021, 34 (2), 634-640. https://doi.org/10.1021/acs.chemrestox.0c00464
9. Wu, Y.; Huo, D.; Chen, G.; Yan, A., SAR and QSAR research on tyrosinase inhibitors using machine learning methods. SAR QSAR Environ. Res. 2021, 32 (2), 85-110. https://doi.org/10.1080/1062936X.2020.1862297
10. Rica, E.; Álvarez, S.; Serratosa, F., Ligand-Based Virtual Screening Based on the Graph Edit Distance. Int. J. Mol. Sci. 2021, 22 (23), 12751. https://doi.org/10.3390/ijms222312751
11. Li, S.; Ding, Y.; Chen, M.; Chen, Y.; Kirchmair, J.; Zhu, Z.; Wu, S.; Xia, J., HDAC3i-Finder: A Machine Learning-based Computational Tool to Screen for HDAC3 Inhibitors. Mol. Inf. 2021, 40 (3), 2000105. https://doi.org/10.1002/minf.202000105
12. Jin, H, Xia, J, Liu, Z, Wang, XS, Zhang, L. A unique ligand-steered strategy for CC chemokine receptor 2 homology modeling to facilitate structure-based virtual screening. Chem Biol Drug Des. 2021; 97: 944– 961. https://doi.org/10.1111/cbdd.13820
13. Garcia-Hernandez, C.; Fernández, A.; Serratosa, F., Learning the Edit Costs of Graph Edit Distance Applied to Ligand-Based Virtual Screening. Curr. Top. Med. Chem. 2020, 20 (18), 1582-1592. https://dx.doi.org/10.2174/1568026620666200603122000
14. Dou, X.; Huang, H.; Li, Y.; Jiang, L.; Wang, Y.; Jin, H.; Jiao, N.; Zhang, L.; Zhang, L.; Liu, Z., Multistage Screening Reveals 3-Substituted Indolin-2-One Derivatives as Novel and Isoform-Selective C-Jun N-Terminal Kinase 3 (Jnk3) Inhibitors: Implications to Drug Discovery for Potential Treatment of Neurodegenerative Diseases. J. Med. Chem., 2019, 62, 6645-6664. https://doi.org/10.1021/acs.jmedchem.9b00537
15. Wang Y.; Dou X.; Jiang L.; Jin H.; Zhang L.; Zhang L.; Liu Z., Discovery of novel glycogen synthase kinase-3α inhibitors: Structure-based virtual screening, preliminary SAR and biological evaluation for treatment of acute myeloid leukemia. Eur. J. Med. Chem., 2019, 171, 221-234. https://doi.org/10.1016/j.ejmech.2019.03.039
16. Garcia-Hernandez, C.; Fernández, A.; Serratosa, F., Ligand-Based Virtual Screening Using Graph Edit Distance as Molecular Similarity Measure. J. Chem. Inf. Model. 2019, 59 (4), 1410-1421. https://doi.org/10.1021/acs.jcim.8b00820
17. Chiba, S.; Ohue, M.; Gryniukova, A.; Borysko, P.; Zozulya, S.; Yasuo, N.; Yoshino, R.; Ikeda, K.; Shin, W.-H.; Kihara, D.; Iwadate, M.; Umeyama, H.; Ichikawa, T.; Teramoto, R.; Hsin, K.-Y.; Gupta, V.; Kitano, H.; Sakamoto, M.; Higuchi, A.; Miura, N.; Yura, K.; Mochizuki, M.; Ramakrishnan, C.; Thangakani, A. M.; Velmurugan, D.; Gromiha, M. M.; Nakane, I.; Uchida, N.; Hakariya, H.; Tan, M.; Nakamura, H. K.; Suzuki, S. D.; Ito, T.; Kawatani, M.; Kudoh, K.; Takashina, S.; Yamamoto, K. Z.; Moriwaki, Y.; Oda, K.; Kobayashi, D.; Okuno, T.; Minami, S.; Chikenji, G.; Prathipati, P.; Nagao, C.; Mohsen, A.; Ito, M.; Mizuguchi, K.; Honma, T.; Ishida, T.; Hirokawa, T.; Akiyama, Y.; Sekijima, M., A prospective compound screening contest identified broader inhibitors for Sirtuin 1. Sci. Rep. 2019, 9 (1), 19585. https://doi.org/10.1038/s41598-019-55069-y
18. Song, Q.; Liu, T.; Liu, Y.; Wang, S.; Fan, C.; Zheng, L.; Bao, Y.; Sun, L.; Yu, C.; Sun, Y.; Song, Z.; Wang, G.; Huang, Y.; Li, Y., An Improved Protocol for the Virtual Screening Discovery of Novel Histone Deacetylase Inhibitors. Chem. Pharm. Bull. 2019, 67 (10), 1076-1081. https://doi.org/10.1248/cpb.c19-00321
19. Kong, Y.; Bender, A.; Yan, A., Identification of Novel Aurora Kinase a (Aurka) Inhibitors Via Hierarchical Ligand-Based Virtual Screening. J. Chem. Inf. Model., 2018, 58, 36-47. https://doi.org/10.1021/acs.jcim.7b00300
20. Zhou, H.; Cao, H.; Skolnick, J., FINDSITEcomb2.0: A New Approach for Virtual Ligand Screening of Proteins and Virtual Target Screening of Biomolecules. J. Chem. Inf. Model. 2018, 58 (11), 2343-2354. https://doi.org/10.1021/acs.jcim.8b00309
21. Dou, X.; Jiang, L.; Wang, Y.; Jin, H.; Liu, Z.; Zhang, L., Discovery of New Gsk-3beta Inhibitors through Structure-Based Virtual Screening. Bioorg. Med. Chem. Lett., 2018, 28, 160-166. https://doi.org/10.1016/j.bmcl.2017.11.036
22. Huang, Yx., Zhao, J., Song, Qh. et al. Virtual screening and experimental validation of novel histone deacetylase inhibitors. BMC Pharmacol Toxicol 17, 32 (2016). https://doi.org/10.1186/s40360-016-0075-8
23. Pei, F., Jin, H., Zhou, X., Xia, J., Sun, L., Liu, Z. and Zhang, L. , Enrichment Assessment of Multiple Virtual Screening Strategies for Toll-Like Receptor 8 Agonists Based on a Maximal Unbiased Benchmarking Data Set. Chem Biol Drug Des, 2015, 86: 1226-1241. https://doi.org/10.1111/cbdd.12590

Contact

Any question or feedback is welcome. Please send emails to jie.william.xia@hotmail.com (Dr. Jie Xia).

Jie Xia, Ph.D.

Associate Professor，Junior PI

Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College

Beijing, China

ORCID: https://orcid.org/0000-0002-9567-3763