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Related Experiment Video

Updated: Mar 6, 2026

Author Spotlight: Enhancing Cryo-Electron Microscopy by Automated Data Collection and Analysis Techniques

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Large-scale virtual screening on public cloud resources with Apache Spark.

Marco Capuccini^1,2, Laeeq Ahmed³, Wesley Schaal²

¹Department of Information Technology, Uppsala University, Box 337, 75105 Uppsala, Sweden.

Journal of Cheminformatics

|March 21, 2017

Summary

This summary is machine-generated.

We developed a scalable method for structure-based virtual screening using Apache Spark. This approach efficiently screens large molecular libraries on cloud resources, achieving 87% parallel efficiency.

Keywords:

Apache Spark Cloud computing Docking Virtual screening

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Area of Science:

Computational chemistry
Bioinformatics

Background:

Structure-based virtual screening (SBVS) is an in-silico method for screening molecular libraries against target receptors.
Traditional docking-based screening of large libraries is computationally intensive.
MapReduce frameworks like Apache Spark offer scalable and fault-tolerant solutions for large-scale data analysis.

Purpose of the Study:

To develop and implement a MapReduce-based approach for parallelizing structure-based virtual screening.
To leverage distributed cloud resources for efficient and scalable molecular screening.

Main Methods:

Developed a method to run existing docking software on distributed cloud resources using Apache Spark.
Implemented the method, named Spark-VS, for parallel SBVS.
Benchmarked the method by docking a target receptor against 2.2 million compounds.

Main Results:

Achieved good parallel efficiency (87%) when running in a public cloud environment.
Demonstrated the scalability of the method for screening large molecular libraries.
Successfully docked a target receptor against a large compound library.

Conclusions:

The developed method enables parallel structure-based virtual screening on cloud or commodity clusters.
The scalability allows for initial testing on smaller libraries before scaling to larger ones.
Spark-VS is available as open-source software.