Enade Perdana Istyastono, Nunung Yuniarti, Maywan Hariono, Sri Hartati Yuliani, Florentinus Dika Octa Riswanto



 Objective: The objective of this study is to construct predictive unbiased structure-based virtual screening (SBVS) protocols to identify potent ligands for estrogen receptor alpha by combining molecular docking, protein-ligand interaction fingerprinting (PLIF), and binary quantitative structure-activity relationship (QSAR) analysis using recursive partition and regression tree method.

Methods: Employing the enhanced version of a directory of useful decoys, SBVS protocols using molecular docking simulations, and PLIF were constructed and retrospectively validated. To avoid bias, SMILES format of the compounds was used. The predictive abilities of the SBVS protocols were then compared based on the enrichment factor (EF) and the F-measure values.

Results: The SBVS protocols resulted in this research were SBVS_1 (employing docking scores of the best pose on every compound to rank the results and selecting compounds within 1% false positives as positive), SBVS_2 (employing decision tree resulted from the binary QSAR analysis using docking scores and PLIF bitstrings of the best pose of every compound as descriptors), and SBVS_3 (employing decision tree resulted from the binary QSAR analysis using ensemble PLIF of the selected poses from optimized docking score as the cutoff). The EF values of SBVS_1, SBVS_2, and SBVS_3 are 28.315, 576.084, and 713.472, respectively, while their F-measure values are 0.310, 0.573, and 0.769, respectively.

Conclusion: Highly predictive unbiased SBVS protocols to identify potent estrogen receptor alpha ligands were constructed. Further application in prospective screening is therefore highly suggested.


Estrogen receptor alpha, Structure-based virtual screening, Recursive partition and regression tree, Molecular docking, Protein-ligand interaction fingerprinting.

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About this article




Pharmaceutical Sciences


Estrogen receptor alpha, Structure-based virtual screening, Recursive partition and regression tree, Molecular docking, Protein-ligand interaction fingerprinting.





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Asian Journal of Pharmaceutical and Clinical Research
Vol 10 Issue 12 December 2017 Page: 206-211

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Authors & Affiliations

Enade Perdana Istyastono
Faculty of Pharmacy Sanata Dharma University

Nunung Yuniarti
Department of Pharmacology and Clinical Pharmacy, Faculty of Pharmacy, Universitas Gadjah Mada

Maywan Hariono
Faculty of Pharmacy Sanata Dharma University

Sri Hartati Yuliani
Faculty of Pharmacy Sanata Dharma University

Florentinus Dika Octa Riswanto
Faculty of Pharmacy Sanata Dharma University

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