In zamanianlab/ZamanianLabVSTools: A Set of Tools and Metrics for Analyzing and Evaluating Virtual Screening Data

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ZamanianLabVSTools

Overview

ZamanianLabVSTools contains a set of utilities used during virtual screening/docking analyses, particularly during evaluation of docking results. Modeling of the data is performed in the tidymodels framework.

Installation

You can install the development version of ZamanianLabVSTools from GitHub with:

# install.packages("devtools")
devtools::install_github("wheelern/ZamanianLabVSTools")

Usage

The primary contribution of this package is during fit evaluation, where some non-standard metrics are used. These have been developed specifically for virtual screening and seek to solve the "early recognition" problem. These metrics can be used to evaluate model performance on hold-out data. Here's an example using the output from a docking run with GNINA, along with a few custom features; these features were then use for fitting with a random forest:

library(ZamanianLabVSTools)
library(dplyr)

data("vs_rf_predictions")

set <- yardstick::metric_set(ef, bedroc, rie)

vs_rf_predictions %>% 
  set(truth, .pred_TRUE)

References

BEDROC: Truchon J-F, Bayly CI. Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem. J Chem Inf Model. 2007 Mar;47(2):488–508.

RIE: Sheridan RP, Singh SB, Fluder EM, Kearsley SK. Protocols for bridging the peptide to nonpeptide gap in topological similarity searches. J Chem Inf Comput Sci. 2001 Sep;41(5):1395–406.

Acknowledgments

Much of the code for bedroc() and rie() was adapted from enrichvs

zamanianlab/ZamanianLabVSTools documentation built on April 16, 2022, 12:53 a.m.