Candidate Glycan-related Drug Prioritization Service
Rely on Our Expertise in Prioritizing Candidate Glycan-related Drugs by Machine Learning
Due to high attrition rates and long time frames for drug development, repurposing drugs (matching existing drugs to new indications) is a solid idea. Machine Learning Models are a good tool for accelerating Glycan Drug Discovery. Combining network embedding and ranking learning, CD BioGlyco utilizes the effectiveness comparison relationship (ECR) between drugs as validation information. By constructing learning models that combine ECRs and drug features, we provide high-quality candidate glycan-related drug prioritization services.
Model construction
Based on quantitative measurements of molecular structures, our lab provides systematic modeling services. Relying on target-specific and training molecular data with structural bioactivity, we construct highly predictive models by machine learning. We utilize network embedding to analyze deep features of learned drugs in heterogeneous drug-disease networks including comparative analyses of efficacy sugar-related drugs. Moreover, we offer cohort construction and screening services for multiple models, delivering the strongest models according to our client's needs.
Drug-drug functional similarity network
Exceptionally, we provide activity analysis at the biological pathway level for candidate glycan-related drugs. We perform Pearson correlation analysis of mRNA pathway or microRNA pathway activity and glycan-related drug activity data. Our researchers provide accurate Pearson correlation coefficients (PCC) (correlation between pathways and drugs).
Based on the synthesized pathway-drug correlation matrix, we provide screening and assessment services for functional similarity between drug activity patterns in all pathways. The relationship between glycan-related drug mRNA and microRNA pathway levels is assessed based on the PCC.
Glycan-related drug prioritizing scores calculation
CD BioGlyco constructs degree-preserving random networks by integrating the random wander and restart algorithm (RWR) on drug similarity networks. Our researchers provide accurate glycan-related drug prioritization score calculation services. We prioritize potential drug candidates that are most likely to be effective against an established target.
Publication
Technology: Global network propagation algorithm, Hypergeometric test, Pearson correlation analysis, Single sample gene set enrichment analysis (ssGSEA)
Journal: Molecular Oncology
Published: 2019
IF: 7.449
Results: In this study, researchers constructed a powerful prioritization of drug candidates (PriorCD) computational method for prioritization of cancer drug candidates. Based on the global network propagation algorithm and the drug-function similarity network of drug activity profiles, the researchers extracted 227 mRNA pathways and 124 microRNA pathways from the mRNA and microRNA expression data using the ssGSEA. Moreover, with the help of cross-validation test and receiver-operating characteristic (ROC) curve analysis, the researchers validated that PriorCD effectively prioritizes cancer drug candidates.
Fig.1 Cell-cell and tissue-of-origin correlation. (Di, et al., 2019)
Applications of Candidate Glycan-related Drug Prioritization
- Candidate glycan-related drug prioritization can be used for modeling and screening of ideal target proteins.
- Candidate glycan-related drug prioritization can be used to screen large chemical structure databases.
- Candidate glycan-related drug prioritization plays a key role in facilitating the repositioning of developed drugs.
- Candidate glycan-related drug prioritization can be used to analyze the interactions between candidate compounds and specific targets.
Advantages of Us
- Our researchers offer a comprehensive range of services for the evaluation of glycan-related drug candidates, including physicochemical property evaluations, structure-activity relationship (SAR) analyses, and target-interaction analyses.
- Our highly trained model development team helps our clients develop satisfactory screening models for glycan-related drugs.
- Our lab has high-throughput analysis systems to ensure fast and reliable delivery.
Frequently Asked Questions
- Why choose candidate glycan-related drug prioritization?
- Candidate glycan-related drug prioritization is an attractive glycan drug discovery program. It has the advantages of speed, efficiency, and low relative risk.
- AI-based candidate glycan-related drug prioritization encodes large amounts of data into parameters and decodes relevant parameters for fast responses.
- Combined with reliable ranking learning models, candidate glycan-related drug prioritization is highly predictable.
- What algorithms are closely related to glycan-related drug candidate prioritization algorithms?
- Algorithms related to the prioritization of glycan-related drug candidates include AI-based algorithms, network diffusion-based algorithms, and network proximity-based algorithms.
CD BioGlyco provides systematic candidate glycan-related drug prioritization services including feature matching, molecular docking, gene pathway, score measurement, and so on. Our researchers provide the most satisfactory solutions according to the needs of our clients. Please feel free to contact us.
Reference
- Di, J.; et al. Prioritization of candidate cancer drugs based on a drug functional similarity network constructed by integrating pathway activities and drug activities. Molecular Oncology. 2019, 13(10): 2259-2277.
For research use only. Not intended for any diagnostic use.
Related Services
