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Predicting Kinase Selectivity Profiles Using Free-Wilson QSAR Analysis
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Human members of the eukaryotic protein kinase family
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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ChemInform Abstract: Predicting Kinase Selectivity Profiles Using Free‐Wilson QSAR Analysis.
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A Free–Wilson‐like Approach to Analyze QSAR Models Based on Graph Decomposition Kernels
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The Use of Qsar and Computational Methods in Drug Design
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Organizing Bioactive Compound Discovery in Target Families
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Application of Free–Wilson Selectivity Analysis for Combinatorial Library Design
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Molecular similarity: Theory, applications, and perspectives
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Small kinase assay panels can provide a measure of selectivity
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Drug/nondrug classification using Support Vector Machines with various feature selection strategies
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Understanding the Impact of the P-loop Conformation on Kinase Selectivity
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Dissecting Kinase Profiling Data to Predict Activity and Understand Cross-Reactivity of Kinase Inhibitors
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Feasibility of Using Molecular Docking-Based Virtual Screening for Searching Dual Target Kinase Inhibitors
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QSAR Models for Predicting the Similarity in Binding Profiles for Pairs of Protein Kinases and the Variation of Models between Experimental Data Sets
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Computational Study of Gleevec and G6G Reveals Molecular Determinants of Kinase Inhibitor Selectivity
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Computational Modeling of Kinase Inhibitor Selectivity
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Molecular dynamics simulations provide insights into the origin of gleevec’s selectivity toward human tyrosine kinases
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Computational analysis of kinase inhibitor selectivity using structural knowledge
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Machine learning approaches to quantitively predict selectivity of compounds against hDAC1 and hDAC6 isoforms
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A theoretical entropy score as a single value to express inhibitor selectivity
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Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors
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Quantitative Structure–Activity Relationship Modeling of Kinase Selectivity Profiles
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Descriptor-Based Profile Analysis of Kinase Inhibitors to Predict Inhibitory Activity and to Grasp Kinase Selectivity
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Prediction of kinase-inhibitor binding affinity using energetic parameters
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Additional file 1 of Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors
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Additional file 2 of Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors
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Death by a thousand cuts through kinase inhibitor combinations that maximize selectivity and enable rational multitargeting
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