J Chem Inf Model - Ligand-based target prediction with signature fingerprints.

Tópicos

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Resumo

When evaluating a potential drug candidate it is desirable to predict target interactions in silico prior to synthesis in order to assess, e.g., secondary pharmacology. This can be done by looking at known target binding profiles of similar compounds using chemical similarity searching. The purpose of this study was to construct and evaluate the performance of chemical fingerprints based on the molecular signature descriptor for performing target binding predictions. For the comparison we used the area under the receiver operating characteristics curve (AUC) complemented with net reclassification improvement (NRI). We created two open source signature fingerprints, a bit and a count version, and evaluated their performance compared to a set of established fingerprints with regards to predictions of binding targets using Tanimoto-based similarity searching on publicly available data sets extracted from ChEMBL. The results showed that the count version of the signature fingerprint performed on par with well-established fingerprints such as ECFP. The count version outperformed the bit version slightly; however, the count version is more complex and takes more computing time and memory to run so its usage should probably be evaluated on a case-by-case basis. The NRI based tests complemented the AUC based ones and showed signs of higher power.

Resumo Limpo

evalu potenti drug candid desir predict target interact silico prior synthesi order assess eg secondari pharmacolog can done look known target bind profil similar compound use chemic similar search purpos studi construct evalu perform chemic fingerprint base molecular signatur descriptor perform target bind predict comparison use area receiv oper characterist curv auc complement net reclassif improv nri creat two open sourc signatur fingerprint bit count version evalu perform compar set establish fingerprint regard predict bind target use tanimotobas similar search public avail data set extract chembl result show count version signatur fingerprint perform par wellestablish fingerprint ecfp count version outperform bit version slight howev count version complex take comput time memori run usag probabl evalu casebycas basi nri base test complement auc base one show sign higher power

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