- Chaput2016.csv[1]
- Cleves2020.csv[2]
- Eberhardt2021.csv[3]
- Mysinger2012.csv[4]
- Wang2019.csv[6]
- Jiang2020.xlsx[10]
- Cleves2019.csv[12]
- Koes2014.csv[13]
- Puertas-Martín2019.csv[14]
- Shen2020.xlsx[15]
- Jiang2021.xlsx[16]
- Jocelyn2021.xlsx[17]
Data set: DUDE
Metric: BEDROC (alpha=80.5)
Software: GOLD,Glide, Surflex and FlexX
Data set: DUDE+
Metric: ROC AUC and ER 1%
Software: Dock, Glide and Surflex
Data set: DUDE
Metric: ROC AUC, BEDROC (alpha=20), EF at 1%, 5% and 10%
Software: AutoDock 1.2
Data set: DUDE
Metric: ROC AUC, logAUC and EF at 1%
Software: DOCK
Data set: DUDE
Metric: ROC AUC and BEDROC (alpha=80.5)
Software: GLIDE
Data set: DUDE
Metric: ROC AUC, BEDROC (alpha=20.0,80.5,321.0) and EF at 0.5%, 1%, 2%, 8%, 20%
Software: AutoPH4
Data set: DUDE
Metric: ROC AUC
Software: Surflex eSim(-pscreen), maximum AUC over the alternate methods
Data set: DUDE
Metric: ROC AUC and BEDROC
Software: USR, ROCS and VAMS
Data set: DUDE
Metric: ROC AUC
Software: OptiPharm and WEGA
Data set: DUDE, DEKOIS2.0, dataset III
Metric: ROC AUC, logAUC, BEDROC(alpha=80.5), EF at 0.1%,0.5%, 1%, 5%
Software: GLIDE, GOLD, LeDock
Data set: DUD-E, LIT-PCBA
Metric: ROC AUC, EF at 1%, 5%, 10%
Software: ROCS、Phase Shape、SHAFTS、WEGA、ShaEP、Shape-it、Align-it、LIGSIFT、LS-align
Data set: DUD-E, LIT-PCBA
Metric: ROC AUC, EF at 1%
Software: GNINA 1.0 with scoring function: Affinity,Pose,Affinity-dense,Pose-dense,Affinity-General,Pose-General,Vina,Vinardo,RFScore-VS,RFScore-4
- metrics.py
- ROCKER[9]
- bootstrap_tldr.py[11]
metrics: ROC AUC, BEDAUC, enrichment_factor(EF) and logAUC
metrics.py can be available from oddt.
ROCKER is a visualization tool for ROC and semi-log ROC curve
ROCKER can be available from: http://www.medchem.fi/rocker
bootstrap_tldr.py is a visualization tool for ROC and semi-log ROC curve
bootstrap_tldr.py can be available from: https://dudez.docking.org
- Chaput, L.; Martinez-Sanz, J.; Saettel, N.; Mouawad, L. Benchmark of Four Popular Virtual Screening Programs: Construction of the Active/Decoy Dataset Remains a Major Determinant of Measured Performance. J. Cheminform. 2016, 8 (1), 56. https://doi.org/10.1186/s13321-016-0167-x.
- Cleves, A. E.; Jain, A. N. Structure- and Ligand-Based Virtual Screening on DUD-E + : Performance Dependence on Approximations to the Binding Pocket. J. Chem. Inf. Model. 2020, 60 (9), 4296–4310. https://doi.org/10.1021/acs.jcim.0c00115.
- Eberhardt, J.; Santos-Martins, D.; Tillack, A. F.; Forli, S. AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings. J. Chem. Inf. Model. 2021, acs.jcim.1c00203. https://doi.org/10.1021/acs.jcim.1c00203.
- Mysinger, M. M.; Carchia, M.; Irwin, J. J.; Shoichet, B. K. Directory of Useful Decoys, Enhanced (DUD-E): Better Ligands and Decoys for Better Benchmarking. J. Med. Chem. 2012, 55 (14), 6582–6594. https://doi.org/10.1021/jm300687e.
- Giangreco, I.; Mukhopadhyay, A.; C. Cole, J. Validation of a Field-Based Ligand Screener Using a Novel Benchmarking Data Set for Assessing 3D-Based Virtual Screening Methods. J. Chem. Inf. Model. 2021, 61 (12), 5841–5852. https://doi.org/10.1021/acs.jcim.1c00866.
- Wang, D.; Cui, C.; Ding, X.; Xiong, Z.; Zheng, M.; Luo, X.; Jiang, H.; Chen, K. Improving the Virtual Screening Ability of Target-Specific Scoring Functions Using Deep Learning Methods. 2019, 10 (August), 1–11. https://doi.org/10.3389/fphar.2019.00924.
- Imrie, F.; Bradley, A. R.; Van Der Schaar, M.; Deane, C. M. Protein Family-Specific Models Using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data. J. Chem. Inf. Model. 2018, 58 (11), 2319–2330. https://doi.org/10.1021/acs.jcim.8b00350.
- Scoring - Calculate rank statistics. http://www.rdkit.org/docs/source/rdkit.ML.Scoring.Scoring.html
- Lätti, S.; Niinivehmas, S.; Pentikäinen, O. T. Rocker: Open Source, Easy-to-Use Tool for AUC and Enrichment Calculations and ROC Visualization. J. Cheminform. 2016, 8 (1), 45. https://doi.org/10.1186/s13321-016-0158-y.
- Jiang, S.; Feher, M.; Williams, C.; Cole, B.; Shaw, D. E. AutoPH4: An Automated Method for Generating Pharmacophore Models from Protein Binding Pockets. J. Chem. Inf. Model. 2020, 60 (9), 4326–4338. https://doi.org/10.1021/acs.jcim.0c00121.
- Stein, R. M.; Yang, Y.; Balius, T. E.; O’Meara, M. J.; Lyu, J.; Young, J.; Tang, K.; Shoichet, B. K.; Irwin, J. J. Property-Unmatched Decoys in Docking Benchmarks. J. Chem. Inf. Model. 2021, 61 (2), 699–714. https://doi.org/10.1021/acs.jcim.0c00598.
- Cleves, A. E.; Johnson, S. R.; Jain, A. N. Electrostatic-Field and Surface-Shape Similarity for Virtual Screening and Pose Prediction. J. Comput. Aided. Mol. Des. 2019, 33 (10), 865–886. https://doi.org/10.1007/s10822-019-00236-6.
- Koes, D. R.; Camacho, C. J. Shape-Based Virtual Screening with Volumetric Aligned Molecular Shapes. J. Comput. Chem. 2014, 35 (25), 1824–1834. https://doi.org/10.1002/jcc.23690.
- Puertas-Martín, S.; Redondo, J. L.; Ortigosa, P. M.; Pérez-Sánchez, H. OptiPharm: An Evolutionary Algorithm to Compare Shape Similarity. Sci. Rep. 2019, 9 (1), 1–24. https://doi.org/10.1038/s41598-018-37908-6.
- Shen, C.; Hu, Y.; Wang, Z.; Zhang, X.; Pang, J.; Wang, G.; Zhong, H.; Xu, L.; Cao, D.; Hou, T. Beware of the Generic Machine Learning-Based Scoring Functions in Structure-Based Virtual Screening. 2020, 00 (April), 1–22. https://doi.org/10.1093/bib/bbaa070.
- Jiang, Z.; Xu, J.; Yan, A.; Wang, L. A Comprehensive Comparative Assessment of 3D Molecular Similarity Tools in Ligand-Based Virtual Screening. Brief. Bioinform. 2021, 22 (6), 1–17. https://doi.org/10.1093/bib/bbab231.
- Sunseri, J.; Koes, D.R. Virtual Screening with Gnina 1.0. Molecules 2021, 26, 7369. https://doi.org/10.3390/molecules26237369
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