RF-Score-VS is a novel Random Forest-based scoring function for Virtual Screening which predicts binding affinity. Its descriptors are based on RF-Score developed by Pedro Ballester et. al. Presented binary implements RF-Score-VS v2, meaning, it counts atoms of certain types within a 12A radius, divided into 2A bins.

Presented repository contains scripts required to reproduce results included in publication introducing RF-Score-VS.

The RF-Score-VS is available as a standalone scoring function with no dependencies required. Usage instructions and detailed information about the binary are available in README.md file alongside binaries and in separate repository.

Download RF-Score-VS for your platform:

• Linux 64-bit
• Windows 64-bit
• MacOS 64-bit

Features used in training of RF-Score-VS are available in head1_full directory. They are stored as compressed CSV files (*.csv.gz) and divided by DUD-e target in subdirectories.

If you want to use all data, we provide a convenient flat CSV files.

Required software:

• Python 2.7
• ODDT 0.2+
• OpenBabel 2.4.1+
• Scikit-Learn 0.17+
• Seaborn
• Pandas

Additional software:

• sklearn-compiledtrees 1.3+ (compiling RFs for final scoring function)
• dask / ipyparallel / ipython-cluster-helper (parallel computations on cluster)

• Wójcikowski M, Ballester PJ, Siedlecki P. Performance of machine-learning scoring functions in structure-based virtual screening. Sci Rep. Nature Publishing Group; 2017;7: 46710. doi:10.1038/srep46710

• Wójcikowski M, Zielenkiewicz P, Siedlecki P. Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field. J Cheminform. 2015;7: 5317. doi:10.1186/s13321-015-0078-2

• Ballester PJ, Mitchell JBO. A machine learning approach to predicting protein-ligand binding affinity with applications to molecular docking. Bioinformatics. 2010;26: 1169–1175. doi:10.1093/bioinformatics/btq112

• Ballester PJ, Schreyer A, Blundell TL. Does a more precise chemical description of protein-ligand complexes lead to more accurate prediction of binding affinity? J Chem Inf Model. 2014;54: 944–955. doi:10.1021/ci500091r

• Li H, Leung K-S, Wong M-H, Ballester PJ. Improving AutoDock Vina Using Random Forest: The Growing Accuracy of Binding Affinity Prediction by the Effective Exploitation of Larger Data Sets. Mol Inform. WILEY-VCH Verlag; 2015;34: 115–126. doi:10.1002/minf.201400132