ArticleNature communications2023
A knowledge-guided pre-training framework for improving molecular representation learning.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
59 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- µORScreen: a lightweight consensus modeling framework for µ-opioid receptor ligand prediction and virtual screening.RSC advances · 2026Article
- Task-adaptive multimodal molecular representations for structure-sensitive property prediction.Chemical science · 2026Article
- A Scalable and Robust Ensemble Deep Learning Method for Predicting Drug-Target Interactions.Interdisciplinary sciences, computational life sciences · 2026Article
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- A unified framework for molecular property prediction based on hierarchical multi-granularity molecular representation learning.Bioinformatics (Oxford, England) · 2026Article
- CBInformax: bioactivity-aware self-supervised molecular representation learning for molecular property and drug-drug interaction prediction.Molecular diversity · 2026Article
- Activity-cliff awareness enables robust graph learning for molecular property prediction.Nature communications · 2026Article
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- Enhancing cross-context generalization in drug perturbation prediction with a multimodal conditional diffusion framework.Bioinformatics (Oxford, England) · 2026Article
- Fast and Flexible 3D Molecular Design Framework for Novel Organic Optoelectronic Materials.JACS Au · 2026Article
- DCPM-ADMET: fusion of dual-component pre-trained model and molecular fingerprints to enhance drug ADMET properties prediction.Journal of cheminformatics · 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
- Oral bioavailability property prediction based on task similarity transfer learning.Molecular diversity · 2026Article
- Transcriptome graph transformer: a graph transformer-based unsupervised model for transcriptome data analysis.BMC bioinformatics · 2026Article
- Bridging antiviral drug discovery with a large language model-powered framework.Communications biology · 2026Article
- Revisiting ADMET prediction reliability under real-world challenges in the foundation model era.Journal of cheminformatics · 2026Article
- Quantum computing applications in drug discovery.Briefings in bioinformatics · 2026Review
- Outer retinal band segmentation in healthy subjects: comparative study between human grading and deep convolutional neural networks.Quantitative imaging in medicine and surgery · 2026Article
- A pipeline for developing AI-driven models to predict molecular initiating events: a case study on neural tube defects.Journal of cheminformatics · 2026Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Learning effective molecular feature representation to facilitate molecular property prediction is of great significance for drug discovery. Recently, there has been a surge of interest in pre-training graph neural networks (GNNs) via self-supervised learning techniques to overcome the challenge of data scarcity in molecular property prediction. However, current self-supervised learning-based methods suffer from two main obstacles: the lack of a well-defined self-supervised learning strategy and the limited capacity of GNNs. Here, we propose Knowledge-guided Pre-training of Graph Transformer (KPGT), a self-supervised learning framework to alleviate the aforementioned issues and provide generalizable and robust molecular representations. The KPGT framework integrates a graph transformer specifically designed for molecular graphs and a knowledge-guided pre-training strategy, to fully capture both structural and semantic knowledge of molecules. Through extensive computational tests on 63 datasets, KPGT exhibits superior performance in predicting molecular properties across various domains. Moreover, the practical applicability of KPGT in drug discovery has been validated by identifying potential inhibitors of two antitumor targets: hematopoietic progenitor kinase 1 (HPK1) and fibroblast growth factor receptor 1 (FGFR1). Overall, KPGT can provide a powerful and useful tool for advancing the artificial intelligence (AI)-aided drug discovery process.
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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.