ArticleScience China. Life sciences2022
Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.
Article in Science China. Life sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Inflammasomes meet organoids and artificial intelligence: unraveling the complexity of gynecological inflammation.Frontiers in immunology · 2026Review
- Privacy-by-Design with Federated Learning will drive future Rare Disease Research.Journal of neuromuscular diseases · 2026Review
- Revolutionizing gastrointestinal cancer research with artificial intelligence: From precision patient stratification to real-world evidence.World journal of gastrointestinal oncology · 2025Review
- Protein lipidation in the tumor microenvironment: enzymology, signaling pathways, and therapeutics.Molecular cancer · 2025Review
- Breaking barriers: we need a multidisciplinary approach to tackle cancer drug resistance.BJC reports · 2025Review
- kMoL: an open-source machine and federated learning library for drug discovery.Journal of cheminformatics · 2025Article
- Federated attention consistent learning models for prostate cancer diagnosis and Gleason grading.Computational and structural biotechnology journal · 2024Article
- A Survey on Computational Methods in Drug Discovery for Neurodegenerative Diseases.Biomolecules · 2024Review
- Review
- FedDdrl: Federated Double Deep Reinforcement Learning for Heterogeneous IoT with Adaptive Early Client Termination and Local Epoch Adjustment.Sensors (Basel, Switzerland) · 2023Article
- Federated learning of molecular properties with graph neural networks in a heterogeneous setting.Patterns (New York, N.Y.) · 2022Article
- Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications.IEEE access : practical innovations, open solutions · 2020Article
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Authors and funding
11 authors.
Funding
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Abstract
Artificial intelligence (AI) models usually require large amounts of high-quality training data, which is in striking contrast to the situation of small and biased data faced by current drug discovery pipelines. The concept of federated learning has been proposed to utilize distributed data from different sources without leaking sensitive information of the data. This emerging decentralized machine learning paradigm is expected to dramatically improve the success rate of AI-powered drug discovery. Here, we simulated the federated learning process with different property and activity datasets from different sources, among which overlapping molecules with high or low biases exist in the recorded values. Beyond the benefit of gaining more data, we also demonstrated that federated training has a regularization effect superior to centralized training on the pooled datasets with high biases. Moreover, different network architectures for clients and aggregation algorithms for coordinators have been compared on the performance of federated learning, where personalized federated learning shows promising results. Our work demonstrates the applicability of federated learning in predicting drug-related properties and highlights its promising role in addressing the small and biased data dilemma in drug discovery.
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