Spark CPVS

Spark-CPVS is a Spark-based library for setting up massively parallel Conformal Prediction Based Virtual Screening (CPVS) in Spark. It is based on earlier Spark-VS Pipelines project https://github.com/mcapuccini/spark-vs. In this project, we use conformal prediction, a mathematical method to filter out poor candidates and only screen ligands which are good leads.

Trying Out on Local

Clone the repo on local

On command line, perform the following command in the vs project directory

mvn clean install -DskipTests

This will install vs project in local maven repositories

Since OpenEye libraries are used under the hood, you need to own and a OpenEye license in order to run this. Therefore, you need to set a OE_LICENSE environment variable that points to the license, in your system to run the code in this repository.

Import vs.examples project in Scala IDE

1. File > Import > General > Existing project into workspace
2. Select spark-cpvs as root directory
3. Click finish
4. Wait for the workspace to build (this can take a while) If the IDE asks to include the scala library or compiler in the workspace click No

If you have scala version problems follow this procedure:

1. Right click on the project folder in the Package Explorer > Properties > Scala Compiler
2. Select fixed scala installation 2.10.X
3. Click apply and let the IDE clean the project

Now you can get familiar with Spark-CPVS giving a look to the examples, and running them in Scala IDE. The main example is DockerWithML. In the data directory you can find an example SDF as well as a receptor file. You will need a larger sdf file and relevant top scores. Remember that in order to run examples you need to specify arguments and OE_LICENSE environment variable through Run Configurations.