ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Leveraging 3D Molecular Spatial Visual Information and Multi-Perspective Representations for Drug Discovery.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- MGCLSyn: multimodal graph contrastive learning for drug synergy prediction.Journal of computer-aided molecular design · 2026Article
- Edge-aware GAT-based protein binding sites prediction.PLoS computational biology · 2026Article
- Graph representation and learning for drug administration prediction.BMC medical informatics and decision making · 2026Article
- A multi-view feature fusion framework with interpretable graph convolution for predicting microbe-drug associations.BMC bioinformatics · 2026Article
- CME-KGDTI: integrating clustered mutations into knowledge graph embedding for drug-target interaction prediction.BioData mining · 2026Article
- Integrating multimodal features with deep learning for protein solubility prediction.Journal of cheminformatics · 2026Article
- Relation-aware context aggregation framework for sparse knowledge graph completion.Scientific reports · 2026Article
- HopWD-DTA: a novel framework for drug-target affinity prediction fusing multi-hop neighborhoods and deep features.Journal of molecular modeling · 2026Article
- A novel method for drug-target affinity prediction by integrating predicted evolutionary information and multi-scale protein graphs.BMC biology · 2026Article
- A dual-branch graph neural network architecture for drug-target binding affinity prediction.Scientific reports · 2026Article
- ConvAHKG: Action-based hybrid knowledge graph with a dual-channel convolutional approach for drug repurposing.Scientific reports · 2026Article
- Integrated transcriptomics and machine learning reveal diagnostic biomarkers and immune-stromal remodeling in ischemic heart failure.Frontiers in bioinformatics · 2026Article
- Enhanced drug-disease association prediction through representation learning on similarity networks.Biology methods & protocols · 2026Article
- Leveraging 3D Molecular Spatial Visual Information and Multi-Perspective Representations for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Robust Association of Breast Cancer Co-morbidity Using Subtype-specific Expressed Genes.Current drug targets · 2026Article
- Discovery of novel peptidomimetics against HSP90-HOP interactions towards improved cancer therapeutics using machine learning strategies.Frontiers in bioinformatics · 2026Article
- 16S ribosomal ribonucleic acid sequencing reveals bile microbiome features in gallstone disease and their links to blood lipid subtypes.Frontiers in microbiology · 2026Article
- Mutational insights andFrontiers in bioinformatics · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Drug discovery remains a costly and time-intensive process, where accurate identification of drug associations is critical for therapeutic development. Existing computational approaches predominantly rely on sequence-derived or 2D molecular representations, often overlooking the intrinsic 3D complexity of small molecules. Here, a deep learning framework is presented that directly learns from 3D molecular spatial visual information, capturing geometric, topological, and stereochemical features from spatial renderings. By integrating this spatial information with traditional molecular descriptors, unified multi-perspective representations are constructed that better reflect molecular structure and function. Across benchmark tasks involving drug-microRNA, drug-drug, and drug-protein interaction prediction, this model consistently outperforms conventional fingerprint-based baselines. Interpretability analyses show that the model attends to biologically relevant substructures, highlighting the value of 3D molecular spatial visual information in molecular recognition. These findings demonstrate the potential of spatially informed learning to enhance predictive performance and provide mechanistic insights in computational drug discovery.
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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.