RF-Score-VS 1.0

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. Further information about reported performance in various scenarios and validation across datasets, see the publication.

Supported platforms

Supported multi-molecular formats:

SDF/MDL (.sdf, .mol)
Mol2 (.mol2)
PDBQT (.pdbqt)
PDB (.pdb)

Usage instructions

Preparations

Download package appropriate for your platform, which contains the binary and sample data to test the RF-Score-VS. To use the scoring function uncompress the archive and open a terminal in the same directory as the binary.

Basic parameters

untagged parameters are treated as docked ligands; user can supply multiple molecular files [required]
-i input file format; if not present then based on extension [optional]
--receptor a protein file; format based on extension [required]
-O output file; if -o is not present file format is based on extension [optional]
-o output file format; if -O is not present then molecules are printed to standard output [optional]

Rescoring

RF-Score-VS predicitons are in -pK units, which means the higher the score the better. To select best binder sort in descending order.

To rescore docked conformations simply run (on Windows omit the leading ./):

./rf-score-vs --receptor protein.pdb ligands.sdf -O ligands_rescored.sdf

Producing CSV files, the RF-Score-VS score is appended to "RFScoreVS_v2" column (additional --field parameter to limit output columns):

./rf-score-vs --receptor protein.pdb ligands.sdf -o csv --field "name" --field "RFScoreVS_v2"

Running test data included with bundle:

./rf-score-vs --receptor test/receptor_rdkit.pdb test/actives_docked.sdf -ocsv

To get the list of all available parameters:

./rf-score-vs --help

NOTE: There is an expected overhead at the beginning of execution of RF-Score-VS binary due to setting up of temporary Python environment.

Binary implementation details and licensing

Single binary is obtained by freezing the Python script with Pyinstaller
Open Drug Discovery Toolkit with OpenBabel
Random forest trees are compiled using sklearn-compiledtrees for extra performance

Licensing

Python environment and all dependencies are prepared using Miniconda with Intel MKL optimizations (see the Miniconda LICENSE)
All components provided here are covered by on permissive 3-clause BSD License.

References:

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

oddt/rfscorevs_binary