Splitting chemical structure data sets for federated privacy-preserving machine learning

Journal of Cheminformatics

Adam Zalewski¹  Wouter Heyndrickx²  Yves Moreau³  Jaak Simm³  Bernd Beck⁴  Lina Humbeck⁴  Ansgar Schuffenhauer⁵  Noe Sturm⁵ 

[1] Amgen Research (Munich) GmbH, Staffelseestraße 2, 81477, Munich, Germany;Janssen Pharmaceutica N.V., Janssen Pharmaceutica, Turnhoutseweg 30, 2340, Beerse, Belgium;KU Leuven, ESAT-STADIUS, Kasteelpark Arenberg 10, 3001, Heverlee, Belgium;Medicinal Chemistry Department, Boehringer Ingelheim Pharma GmbH & Co. KG, Birkendorfer Str. 65, 88397, Biberach an der Riss, Germany;Novartis Institutes for BioMedical Research, Novartis Campus, CH-4002, Basel, Switzerland;

关键词: Cross-validation;    Train-test-split;    Federated machine learning;    Leader follower clustering;    Sphere exclusion clustering;    Locality-sensitive hashing;    Scaffold tree;    Scaffold network;    ChemFold;   

DOI  :  10.1186/s13321-021-00576-2

来源: Springer

【 摘 要 】

With the increase in applications of machine learning methods in drug design and related fields, the challenge of designing sound test sets becomes more and more prominent. The goal of this challenge is to have a realistic split of chemical structures (compounds) between training, validation and test set such that the performance on the test set is meaningful to infer the performance in a prospective application. This challenge is by its own very interesting and relevant, but is even more complex in a federated machine learning approach where multiple partners jointly train a model under privacy-preserving conditions where chemical structures must not be shared between the different participating parties. In this work we discuss three methods which provide a splitting of a data set and are applicable in a federated privacy-preserving setting, namely: a. locality-sensitive hashing (LSH), b. sphere exclusion clustering, c. scaffold-based binning (scaffold network). For evaluation of these splitting methods we consider the following quality criteria (compared to random splitting): bias in prediction performance, classification label and data imbalance, similarity distance between the test and training set compounds. The main findings of the paper are a. both sphere exclusion clustering and scaffold-based binning result in high quality splitting of the data sets, b. in terms of compute costs sphere exclusion clustering is very expensive in the case of federated privacy-preserving setting.

【 授权许可】

CC BY