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Splitting chemical structure data sets for federated privacy-preserving machine learning
- Source :
- Journal of Cheminformatics, Vol 13, Iss 1, Pp 1-14 (2021), Journal of Cheminformatics
- Publication Year :
- 2021
- Publisher :
- BMC, 2021.
-
Abstract
- 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. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-021-00576-2.
- Subjects :
- Technology
Multiple Partners
Scaffold tree
Computer science
media_common.quotation_subject
Chemistry, Multidisciplinary
Scaffold network
Hash function
Information technology
Library and Information Sciences
Data imbalance
Machine learning
computer.software_genre
Leader follower clustering
DRUGS
Quality (business)
Sphere exclusion clustering
Physical and Theoretical Chemistry
Cluster analysis
QD1-999
media_common
Locality-sensitive hashing
Train-test-split
Science & Technology
Computer Science, Information Systems
business.industry
Cross-validation
T58.5-58.64
Computer Graphics and Computer-Aided Design
LIBRARIES
Computer Science Applications
Data set
Privacy preserving
Chemistry
Federated machine learning
Test set
ChemFold
Physical Sciences
Computer Science
Computer Science, Interdisciplinary Applications
Artificial intelligence
business
computer
Research Article
Subjects
Details
- Language :
- English
- ISSN :
- 17582946
- Volume :
- 13
- Issue :
- 1
- Database :
- OpenAIRE
- Journal :
- Journal of Cheminformatics
- Accession number :
- edsair.doi.dedup.....74e97847b186b2d2ad8d219933dee3c4