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

Journal Contribution - Journal Article

© 2016 Elsevier B.V. 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.
Journal: Future Generation Computer Systems-The International Journal of eScience
ISSN: 0167-739X
Volume: 67
Pages: 409 - 417
Publication year:2017
BOF-publication weight:6
CSS-citation score:2
Authors from:Government, Private, Higher Education