ArticleJournal of chemical information and modeling2024
MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
64 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Securing Federated Learning With Blockchain in the Medical Field: Systematic Literature Review.Journal of medical Internet research · 2026Pooled it
- Federated machine learning in healthcare: A systematic review on clinical applications and technical architecture.Cell reports. Medicine · 2024Pooled it
- TechBio 3.0 closes the drug discovery loop with multimodal AI and generative chemistry.npj drug discovery · 2026Review
- A critical appraisal of structure-based virtual screening, binding free energy methods, and AI-driven molecular design across the evolving computational landscape of drug discovery.Journal of computer-aided molecular design · 2026Review
- Physical priors improve performance of structure-based binding affinity models.bioRxiv : the preprint server for biology · 2026Article
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Gaps in AI-Driven Pharmacokinetic Property Prediction for Early Drug Development: A Scoping Review.Journal of chemical information and modeling · 2026Article
- Quantum-Augmented Federated AI for Adaptive Pharmacogenomic Precision Oncology: A Perspective.ACS pharmacology & translational science · 2026Review
- Censoring chemical data to mitigate dual use risk.Digital discovery · 2026Article
- Artificial intelligence in drug discovery - what it is, where we stand and the path forward.Nature reviews. Drug discovery · 2026Review
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Stabilization strategies and advancements in lyophilization to preserve integrity and efficacy of next-generation biologicals.International journal of pharmaceutics: X · 2026Review
- Beyond petrochemicals: challenges and opportunities in industrial-scale biomanufacturing.Nature communications · 2026Review
- Mapping the avoid-ome: a systematic open-science approach to predictive ADMET.Nature communications · 2026Review
- Ocular Lymphatics in Health and Disease.Diagnostics (Basel, Switzerland) · 2026Review
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- In Silico Regression Modeling and Improved Interpretability To Predict the Transport Inhibitory Activity of Breast Cancer Resistance Protein.ACS omega · 2026Article
- Evaluating generalization in protein-ligand cofolding methods.Nature structural & molecular biology · 2026Article
- What's new in digital and computational pathology 2026: advances in adoption, standards, AI technologies, and clinical integration.Journal of pathology and translational medicine · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
4 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
49 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security. The experiments involved an unprecedented cross-pharma data set of 2.6+ billion confidential experimental activity data points, documenting 21+ million physical small molecules and 40+ thousand assays in on-target and secondary pharmacodynamics and pharmacokinetics. Appropriate complementary metrics were developed to evaluate the predictive performance in the federated setting. In addition to predictive performance increases in labeled space, the results point toward an extended applicability domain in federated learning. Increases in collective training data volume, including by means of auxiliary data resulting from single concentration high-throughput and imaging assays, continued to boost predictive performance, albeit with a saturating return. Markedly higher improvements were observed for the pharmacokinetics and safety panel assay-based task subsets.
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Registered trials
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.