ArticleScientific reports2016
Improved genome-scale multi-target virtual screening via a novel collaborative filtering approach to cold-start problem.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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
21 citing papers in PubMed.
- Predicting drug protein interactions based on improved support vector data description in unbalanced data.BioImpacts : BI · 2025Article
- PrePCI: A structure- and chemical similarity-informed database of predicted protein compound interactions.Protein science : a publication of the Protein Society · 2023Article
- Hyperbolic matrix factorization improves prediction of drug-target associations.Scientific reports · 2023Article
- DRPADC: A novel drug repositioning algorithm predicting adaptive drugs for COVID-19.Computers & chemical engineering · 2022Article
- AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification.Briefings in bioinformatics · 2022Article
- Detecting Drug-Target Interactions with Feature Similarity Fusion and Molecular Graphs.Biology · 2022Article
- Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions.Patterns (New York, N.Y.) · 2021Article
- Coating matching recommendation based on improved fuzzy comprehensive evaluation and collaborative filtering algorithm.Scientific reports · 2021Article
- MSA-Regularized Protein Sequence Transformer toward Predicting Genome-Wide Chemical-Protein Interactions: Application to GPCRome Deorphanization.Journal of chemical information and modeling · 2021Article
- AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders.BMC bioinformatics · 2021Article
- Machine learning approaches and databases for prediction of drug-target interaction: a survey paper.Briefings in bioinformatics · 2021Review
- Database of adverse events associated with drugs and drug combinations.Scientific reports · 2019Article
- Biological representation of chemicals using latent target interaction profile.BMC bioinformatics · 2019Article
- Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.Briefings in bioinformatics · 2019Review
- Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.Briefings in bioinformatics · 2019Review
- A Novel Approach for Drug-Target Interactions Prediction Based on Multimodal Deep Autoencoder.Frontiers in pharmacology · 2019Article
- MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies Applied to Molecular Informatics.BioMed research international · 2019Article
- DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.Bioinformatics (Oxford, England) · 2018Article
- Predicting serious rare adverse reactions of novel chemicals.Bioinformatics (Oxford, England) · 2018Article
- DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.Bioinformatics (Oxford, England) · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Conventional one-drug-one-gene approach has been of limited success in modern drug discovery. Polypharmacology, which focuses on searching for multi-targeted drugs to perturb disease-causing networks instead of designing selective ligands to target individual proteins, has emerged as a new drug discovery paradigm. Although many methods for single-target virtual screening have been developed to improve the efficiency of drug discovery, few of these algorithms are designed for polypharmacology. Here, we present a novel theoretical framework and a corresponding algorithm for genome-scale multi-target virtual screening based on the one-class collaborative filtering technique. Our method overcomes the sparseness of the protein-chemical interaction data by means of interaction matrix weighting and dual regularization from both chemicals and proteins. While the statistical foundation behind our method is general enough to encompass genome-wide drug off-target prediction, the program is specifically tailored to find protein targets for new chemicals with little to no available interaction data. We extensively evaluate our method using a number of the most widely accepted gene-specific and cross-gene family benchmarks and demonstrate that our method outperforms other state-of-the-art algorithms for predicting the interaction of new chemicals with multiple proteins. Thus, the proposed algorithm may provide a powerful tool for multi-target drug design.
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.