ArticleNature communications2022
A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 1 of them a synthesis that pooled it.
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Who cites it
39 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of molecular representation learning foundation models.Briefings in bioinformatics · 2026Pooled it
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- Mol-CADiff: text-conditional molecule generation via causality-aware autoregressive diffusion.Nature communications · 2026Article
- Navigating chemical-linguistic sharing space with heterogeneous molecular encoding.Nature communications · 2026Article
- Machine Learning-Empowered Electromagnetic Wave Absorbing Materials: From Forward Prediction to Generative Inverse Design.Molecules (Basel, Switzerland) · 2026Review
- A pretrained biomedical large language model for Persian biomedical text mining.Scientific reports · 2026Article
- Transforming Molecular Science With Large Language Models: From Molecule Understanding to Autonomous Scientific Discovery.Chemistry, an Asian journal · 2026Review
- Extracellular vesicle and particle biomarkers in cancer: a machine learning blueprint for liquid biopsy.Journal of nanobiotechnology · 2026Review
- ITMol: a molecular image-text foundation model bridging the semantic gap for property prediction and retrieval.BMC biology · 2026Article
- Multi-to-uni modal knowledge transfer pre-training for molecular representation learning.Nature communications · 2026Article
- Multi-Modal Fusion Frameworks of Subgraph-Optimized Graph Autoencoder for Molecular Property Prediction.Journal of chemical information and modeling · 2026Article
- A survey of contrastive learning methods in molecular representation.Briefings in bioinformatics · 2026Review
- Multimodal pre-training models of molecular representation for drug discovery.National science review · 2026Review
- Addressing infectious diseases in Africa by accelerating drug discovery through data science.Communications medicine · 2025Review
- MolPrompt: improving multi-modal molecular pre-training with knowledge prompts.Bioinformatics (Oxford, England) · 2025Article
- Physics-Based Solubility Prediction for Organic Molecules.Chemical reviews · 2025Review
- Heuristic optimization in classification atoms in molecules using GCN via uniform simulated annealing.Scientific reports · 2025Article
- Sculpting molecules in text-3D space: a flexible substructure aware framework for text-oriented molecular optimization.BMC bioinformatics · 2025Article
- A Perspective on Foundation Models in Chemistry.JACS Au · 2025Review
- Discovery of diverse and high-quality mRNA capping enzymes through a language model-enabled platform.Science advances · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile reading on both molecule structure and biomedical text information, we propose a knowledgeable machine reading system that bridges both types of information in a unified deep-learning framework for comprehensive biomedical research assistance. We solve the problem that existing machine reading models can only process different types of data separately, and thus achieve a comprehensive and thorough understanding of molecule entities. By grasping meta-knowledge in an unsupervised fashion within and across different information sources, our system can facilitate various real-world biomedical applications, including molecular property prediction, biomedical relation extraction and so on. Experimental results show that our system even surpasses human professionals in the capability of molecular property comprehension, and also reveal its promising potential in facilitating automatic drug discovery and documentation in the future.
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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.