ArticleFrontiers in chemistry2019
Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network.
Article in Frontiers in chemistry, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- KGTC-DDA:Drug-disease association prediction based on parallel graph isomorphism and transformer networks with cross-attention.Biochemistry and biophysics reports · 2026Article
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- The Scalp Microbiome-Hair Axis: Mechanisms and Therapeutic Translation.Research (Washington, D.C.) · 2026Review
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.Bioinformatics (Oxford, England) · 2025Article
- A comprehensive landscape of AI applications in broad-spectrum drug interaction prediction: a systematic review.Journal of cheminformatics · 2025Review
- Strategies for robust, accurate, and generalizable benchmarking of drug discovery platforms.bioRxiv : the preprint server for biology · 2025Article
- Improving drug repositioning with negative data labeling using large language models.Journal of cheminformatics · 2025Article
- Bayesian Inference for Drug Discovery by High Negative Samples and Oversampling.Bioinformatics and biology insights · 2025Article
- DRML-Ensemble: drug repurposing method based on feature construction of multi-layer ensemble.Journal of molecular modeling · 2024Article
- Predicting drug-drug interactions based on multi-view and multichannel attention deep learning.Health information science and systems · 2023Article
- Drug repositioning based on weighted local information augmented graph neural network.Briefings in bioinformatics · 2023Article
- Identification of Potential Parkinson's Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network.Molecules (Basel, Switzerland) · 2022Article
- A Discovery Strategy for Active Compounds of Chinese Medicine Based on the Prediction Model of Compound-Disease Relationship.Journal of oncology · 2022Article
- Drug Repositioning with GraphSAGE and Clustering Constraints Based on Drug and Disease Networks.Frontiers in pharmacology · 2022Article
- A New Framework for Discovering Protein Complex and Disease Association via Mining Multiple Databases.Interdisciplinary sciences, computational life sciences · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore, it is urgent to develop computational method for predicting potential drug-disease associations on a large scale. Herein, a novel method was proposed to identify drug-disease associations based on the deep learning technique. Molecular structure and clinical symptom information were used to characterize drugs and diseases. Then, a novel two-dimensional matrix was constructed and mapped to a gray-scale image for representing drug-disease association. Finally, deep convolution neural network was introduced to build model for identifying potential drug-disease associations. The performance of current method was evaluated based on the training set and test set, and accuracies of 89.90 and 86.51% were obtained. Prediction ability for recognizing new drug indications, lead compounds and true drug-disease associations was also investigated and verified by performing various experiments. Additionally, 3,620,516 potential drug-disease associations were identified and some of them were further validated through docking modeling. It is anticipated that the proposed method may be a powerful large scale virtual screening tool for drug research and development. The source code of MATLAB is freely available on request from the authors.
Indexed as
Identifiers
What OpenQuestion holds
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.