ArticleJournal of cheminformatics2021
Splitting chemical structure data sets for federated privacy-preserving machine learning.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Global descriptors informed learning for organic melting-point prediction.RSC advances · 2026Article
- Cardiosim-Tox: an interpretable multitask deep learning QSAR platform with multimodal feature fusion for predicting hERG, Cav1.2, and Nav1.5 blockade risk and potency.Archives of toxicology · 2026Article
- CheMLT-F: multitask learning in biochemistry through transformer fusion.Journal of cheminformatics · 2026Article
- Integration of Machine Learning With PBPK and QSAR Modeling Approaches to Facilitate Drug Discovery and Development.CPT: pharmacometrics & systems pharmacology · 2026Review
- Collision-free morgan fingerprints: a principled approach to enhance machine learning performance and interpretability in chemistry.Journal of cheminformatics · 2026Article
- Machine Learning Exploration of Food-Derived Chemical Space for Potential Nutritional Metabolic Regulators Targeting Dipeptidyl Peptidase-4.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Unveiling optimal molecular features for hERG insights with automatic machine learning.Journal of pharmaceutical analysis · 2025Article
- Chemical knowledge-informed framework for privacy-aware retrosynthesis learning.Nature communications · 2025Article
- iSIM-Sigma: Efficient Standard Deviation Calculation for Molecular Similarity.Journal of chemical information and modeling · 2025Article
- The development and validation of a privacy-preserving model based on federated learning for diagnosing severe pediatric pneumonia.Translational pediatrics · 2025Article
- Predicting Pharmacokinetics in Rats Using Machine Learning: A Comparative Study Between Empirical, Compartmental, and PBPK-Based Approaches.Clinical and translational science · 2025Article
- Predicting cell morphological responses to perturbations using generative modeling.Nature communications · 2025Article
- Machine learning prediction of intestinal α-glucosidase inhibitors using a diverse set of ligands: a drug repurposing effort with drugBank database screening.In silico pharmacology · 2025Article
- MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.Journal of chemical information and modeling · 2024Article
- Yves Moreau has received the 2023 Einstein Foundation Individual Award for Promoting Quality in Research.Bioinformatics advances · 2024Article
- Computational workflow for discovering small molecular binders for shallow binding sites by integrating molecular dynamics simulation, pharmacophore modeling, and machine learning: STAT3 as case study.Journal of computer-aided molecular design · 2023Article
- A benchmark dataset for machine learning in ecotoxicology.Scientific data · 2023Article
- Characterizing Uncertainty in Machine Learning for Chemistry.Journal of chemical information and modeling · 2023Article
- Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK).Journal of cheminformatics · 2022Article
- High performance of privacy-preserving acute myocardial infarction auxiliary diagnosis based on federated learning: a multicenter retrospective study.Annals of translational medicine · 2022Article
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8 authors.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.