SuperPred: drug classification and target prediction
نویسندگان
چکیده
UNLABELLED The drug classification scheme of the World Health Organization (WHO) [Anatomical Therapeutic Chemical (ATC)-code] connects chemical classification and therapeutic approach. It is generally accepted that compounds with similar physicochemical properties exhibit similar biological activity. If this hypothesis holds true for drugs, then the ATC-code, the putative medical indication area and potentially the medical target should be predictable on the basis of structural similarity. We have validated that the prediction of the drug class is reliable for WHO-classified drugs. The reliability of the predicted medical effects of the compounds increases with a rising number of (physico-) chemical properties similar to a drug with known function. The web-server translates a user-defined molecule into a structural fingerprint that is compared to about 6300 drugs, which are enriched by 7300 links to molecular targets of the drugs, derived through text mining followed by manual curation. Links to the affected pathways are provided. The similarity to the medical compounds is expressed by the Tanimoto coefficient that gives the structural similarity of two compounds. A similarity score higher than 0.85 results in correct ATC prediction for 81% of all cases. As the biological effect is well predictable, if the structural similarity is sufficient, the web-server allows prognoses about the medical indication area of novel compounds and to find new leads for known targets. AVAILABILITY the system is freely accessible at http://bioinformatics.charite.de/superpred. SuperPred can be obtained via a Creative Commons Attribution Noncommercial-Share Alike 3.0 License.
منابع مشابه
SuperPred: update on drug classification and target prediction
The SuperPred web server connects chemical similarity of drug-like compounds with molecular targets and the therapeutic approach based on the similar property principle. Since the first release of this server, the number of known compound-target interactions has increased from 7000 to 665,000, which allows not only a better prediction quality but also the estimation of a confidence. Apart from ...
متن کاملO-3: Drug Repositioning by Merging Gene Expression Data Analysis and Cheminformatics Target Prediction Approaches
The transcriptional responses of drug treatments combined with a protein target prediction algorithm was utilised to associate compounds to biological genomic space. This enabled us to predict efficacy of compounds in cMap and LINCS against 181 databases of diseases extracted from GEO. 18/30 of top drugs predicted for leukemia (e.g. Leflunomide and Etoposide) and breast cancer (e.g. Tamoxifen a...
متن کاملSupervised prediction of drug-target interactions by ensemble learning
Drug-target interaction (DTI) provides novel insights about the genomic drug discovery. The wet experiments of identifying DTIs are time-consuming and costly. Recently, the increase of available data provides the opportunity to the development of computational methods. Although many computational methods have been proposed (such as classification-based methods, graph-based methods and network-b...
متن کاملClassification and its application to drug-target interaction prediction
Classification is one of the most popular and widely used supervised learning tasks, which categorizes objects into predefined classes based on known knowledge. Classification has been an important research topic in machine learning and data mining. Different classification methods have been proposed and applied to deal with various real-world problems. Unlike unsupervised learning such as clus...
متن کاملToward more realistic drug–target interaction predictions
A number of supervised machine learning models have recently been introduced for the prediction of drug-target interactions based on chemical structure and genomic sequence information. Although these models could offer improved means for many network pharmacology applications, such as repositioning of drugs for new therapeutic uses, the prediction models are often being constructed and evaluat...
متن کامل