Scaling machine learning for target prediction in drug discovery using Apache Spark

• Published in: Future generation computer systems 2017-02, Vol.67, p.409-417
• Main Authors: Harnie, Dries, Saey, Mathijs, Vapirev, Alexander E., Wegner, Jörg Kurt, Gedich, Andrey, Steijaert, Marvin, Ceulemans, Hugo, Wuyts, Roel, De Meuter, Wolfgang
• Format: Article
• Language: English
• Subjects: Apache Spark; Drug discovery; Machine learning; Target prediction
• ISSN: 0167-739X; 1872-7115
• DOI: 10.1016/j.future.2016.04.023

In the context of drug discovery, a key problem is the identification of candidate molecules that affect proteins associated with diseases. Inside Janssen Pharmaceutica, the Chemogenomics project aims to derive new candidates from existing experiments through a set of machine learning predictor programs, written in single-node C++. These programs take a long time to run and are inherently parallel, but do not use multiple nodes. We show how we reimplemented the pipeline using Apache Spark, which enabled us to lift the existing programs to a multi-node cluster without making changes to the predictors. We have benchmarked our Spark pipeline against the original, which shows almost linear speedup up to 8 nodes. In addition, our pipeline generates fewer intermediate files while allowing easier checkpointing and monitoring. •We have used Spark to automatically distribute C++ predictors over a cluster.•Our Spark application allows near-linear speedup and optimal cluster utilization.•The core of the algorithm is easily changed to allow for experimentation.