ArticleNature communications2024
Transfer learning with graph neural networks for improved molecular property prediction in the multi-fidelity setting.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed.
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- GeoPep: A Geometry-Aware Masked Language Model for Protein-Peptide Binding Site Prediction.Journal of chemical information and modeling · 2026Article
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- A multi-fidelity tabular prior-data fitted network model for accurate prediction and uncertainty quantification.Nature communications · 2026Article
- Unifying pJournal of chemical information and modeling · 2026Article
- The Tsetlin Machine: A "Third Way" in QSAR Modeling.Journal of chemical information and modeling · 2026Article
- PolyT-GNN: A Graph Neural Network Framework for Data-Driven Discovery of High-Temperature Two-Way Shape Memory Polymers.ACS applied materials & interfaces · 2026Article
- Integrated computational and experimental analysis explores FOLH1 expression patterns across cancers and nominates melatonin as a potential modulator in prostate cancer models.PLoS computational biology · 2026Article
- Transfer learning of GW Bethe-Salpeter equation excitation energies.Chemical science · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Discovering anti-obesity blue food compounds via combined deep learning and in silico approaches.Molecular diversity · 2026Article
- AI-driven drug-target interaction prediction: current progress, challenges, and future roadmap for precision medicine.Journal of computer-aided molecular design · 2026Review
- Spectral Decomposition of Chemical Semantics for Activity Cliffs-Aware Molecular Property Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Mol2Raman: a graph neural network model for predicting Raman spectra from SMILES representations.Digital discovery · 2026Article
- A unified active learning framework for photosensitizer design.Chemical science · 2026Article
- PhaseTransfer: A transfer learning framework for efficient phase diagram mapping.npj computational materials · 2026Article
- AI-driven molecular modeling and design: from property prediction to drug generation.Journal of computer-aided molecular design · 2025Article
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- Improving polygenic risk score based drug response prediction using transfer learning.NPJ genomic medicine · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property of interest. This problem arises in discovery processes that rely on screening funnels for trading off the overall costs against throughput and accuracy. Typically, individual stages in these processes are loosely connected and each one generates data at different scale and fidelity. We consider this setup holistically and demonstrate empirically that existing transfer learning techniques for graph neural networks are generally unable to harness the information from multi-fidelity cascades. Here, we propose several effective transfer learning strategies and study them in transductive and inductive settings. Our analysis involves a collection of more than 28 million unique experimental protein-ligand interactions across 37 targets from drug discovery by high-throughput screening and 12 quantum properties from the dataset QMugs. The results indicate that transfer learning can improve the performance on sparse tasks by up to eight times while using an order of magnitude less high-fidelity training data. Moreover, the proposed methods consistently outperform existing transfer learning strategies for graph-structured data on drug discovery and quantum mechanics datasets.
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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.