title: Shared Consensus Machine Learning Models for Predicting Blood Stage Malaria Inhibition.
abstract: The development of new antimalarial therapies is essential, and lowering the barrier of entry for the screening and discovery of new lead compound classes can spur drug development at organizations that may not have large compound screening libraries or resources to conduct high-throughput screens. Machine learning models have been long established to be more robust and have a larger domain of applicability with larger training sets. Screens over multiple data sets to find compounds with potential malaria blood stage inhibitory activity have been used to generate multiple Bayesian models. Here we describe a method by which Bayesian quantitative structure-activity relationship models, which contain information on thousands to millions of proprietary compounds, can be shared between collaborators at both for-profit and not-for-profit institutions. This model-sharing paradigm allows for the development of consensus models that have increased predictive power over any single model and yet does not reveal the identity of any compounds in the training sets.
authors.affiliation: Merck & Co., Inc. , Kenilworth, New Jersey 07033, United States.
authors.forename: Andreas
authors.identifier: 0000-0001-5353-4784
authors.initials: A
authors.lastname: Verras
authors.affiliation: Merck & Co., Inc. , Boston, Massachusetts 02210, United States.
authors.forename: Chris L
authors.initials: CL
authors.lastname: Waller
authors.affiliation: Novartis Institute for Tropical Diseases Pte. Ltd. , Singapore 138670, Singapore.
authors.forename: Peter
authors.identifier: 0000-0002-4111-3162
authors.initials: P
authors.lastname: Gedeck
authors.affiliation: GlaxoSmithKline , Stevenage SG1 2NY, United Kingdom.
authors.forename: Darren V S
authors.initials: DV
authors.lastname: Green
authors.affiliation: AstraZeneca , Gothenburg 431 83, Sweden.
authors.forename: Thierry
authors.initials: T
authors.lastname: Kogej
authors.affiliation: AstraZeneca India Pvt. Ltd. , Bangalore 560045, India.
authors.forename: Anandkumar
authors.initials: A
authors.lastname: Raichurkar
authors.affiliation: AstraZeneca India Pvt. Ltd. , Bangalore 560045, India.
authors.forename: Manoranjan
authors.initials: M
authors.lastname: Panda
authors.affiliation: Chemical Biology and Therapeutics Department, St. Jude Children's Research Hospital , Memphis, Tennessee 38105, United States.
authors.forename: Anang A
authors.initials: AA
authors.lastname: Shelat
authors.affiliation: Chemical Biology and Therapeutics Department, St. Jude Children's Research Hospital , Memphis, Tennessee 38105, United States.
authors.forename: Julie
authors.initials: J
authors.lastname: Clark
authors.affiliation: Chemical Biology and Therapeutics Department, St. Jude Children's Research Hospital , Memphis, Tennessee 38105, United States.
authors.forename: R Kiplin
authors.identifier: 0000-0002-9638-2060
authors.initials: RK
authors.lastname: Guy
authors.affiliation: European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Trust Genome Campus , Hinxton, Cambridgeshire CB10 1SD, United Kingdom.
authors.forename: George
authors.initials: G
authors.lastname: Papadatos
authors.affiliation: Medicines for Malaria Ventures Discovery , Geneva 1215, Switzerland.
authors.forename: Jeremy
authors.initials: J
authors.lastname: Burrows
mesh_terms: D000962:Antimalarials / Q000494:pharmacology* / Q000627:therapeutic use; D001499:Bayes Theorem; D055808:Drug Discovery; D000069550:Machine Learning; D008288:Malaria / Q000097:blood / Q000188:drug therapy*; D008962:Models, Theoretical; D021281:Quantitative Structure-Activity Relationship; D012372:ROC Curve; D013696:Temperature
chemical_list: D000962:Antimalarials