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Network pharmacology detetection software tools | Drug discovery data analysis

Network pharmacology-based prediction of multi-targeted drug combinations is becoming a promising strategy to improve anticancer efficacy and safety.

Source text:
(He et al., 2015) TIMMA-R: an R package for predicting synergistic multi-targeted drug combinations in cancer cell lines or patient-derived samples. Bioinformatics.

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A web service enabling drug developers to carry out network pharmacology-based prediction and analysis by integrating results from structural biology with systems biology. Its user-friendly GUI interface simplifies essential operations for large-scale screening. Using the predictive docking approach, systemsDock can test a large number of target proteins with good prediction accuracy. This will reduce the number of tests for bioassay. Together with a curated pathway map, systemsDock helps to comprehensively characterize the underlying mechanism of a drug candidate and to interpret its cascading effects, improving the prediction of drug efficacy and safety.
Provides a database of chemical tool compounds for targets from medicinal chemistry literature and patents. PROBELIST was developed to help researchers make the best use of these chemicals and to easily locate useful functional inhibitors for a given biological pathway. The data set was annotated in a target-centric way, with the KEGG pathways, gene ontology (GO) terms (molecular function (MF), biological process (BP), and cellular compartment (CC)), reactome pathways, Entrez gene IDs, and Uniprot IDs for each protein target.
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