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• W2802536107 abstract "Protein kinases form a consistent class of promising drug targets, and several efforts have been made to predict the activities of small molecules against a representative part of the kinome. This study continues our previous work ( Bora , A. ; Avram , S. ; Ciucanu , I. ; Raica , M. ; Avram , S. Predictive Models for Fast and Effective Profiling of Kinase Inhibitors . J. Chem. Inf.2016 , 56 , 895 - 905 ; www.chembioinf.ro ) aiming to build and measure the performance of ligand-based kinase inhibitor prediction models. Here we analyzed kinase-inhibitor pairs with multiple activity points extracted from the ChEMBL database and identified the main sources of inconsistency. Our results indicate that lower IC50 values are usually less affected by errors and reflect more accurately the structure-activity relationship of the molecules against the target, ideally for quantitative structure-activity relationship studies. Further, we modeled the activities of 104 kinases using unbiased target-specific activity points. The performance of predictors built on extended connectivity fingerprints (ECFP4) and two-dimensional pharmacophore fingerprints (PFPs) are compared by means of tolerance intervals (TIs) (95%/95%) in virtual screening (VS) and classification tasks using external random ( RandSets) and diversity-based ( DivSets) test sets. We found that the two encodings perform superior to each other on different kinases in VS and that PFP models perform consistently better in classifying actives (higher sensitivity). Next, we combined the two encodings into a single one (PFPECFP) and demonstrated that especially in VS (as indicated by the exponential receiver operating curve enrichment metric (eROCE)), for the vast majority of kinases the model performance increased compared with the individual fingerprint models. These findings are highlighted in the more challenging DivSets compared with RandSets. The current paper explores the boundaries of inhibitor predictors for individual kinases to enhance VS and ultimately aid the discovery of novel compounds with desirable polypharmacology." @default.
• W2802536107 created "2018-05-17" @default.
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• W2802536107 date "2018-04-30" @default.
• W2802536107 modified "2023-10-16" @default.
• W2802536107 title "Modeling Kinase Inhibition Using Highly Confident Data Sets" @default.
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• W2802536107 doi "https://doi.org/10.1021/acs.jcim.7b00729" @default.
• W2802536107 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29708742" @default.
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