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Dissecting Kinase Profiling Data to Predict Activity and Understand Cross-Reactivity of Kinase Inhibitors

https://doi.org/10.1021/ci200607f

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Bayesian Methods in Virtual Screening and Chemical Biology

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Kinomics—structural biology and chemogenomics of kinase inhibitors and targets

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Graph kernels for chemical informatics

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Kinomics: characterizing the therapeutically validated kinase space

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Active Learning with Support Vector Machines in the Drug Discovery Process

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Collaborative Filtering on a Family of Biological Targets

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Prediction of Biological Targets for Compounds Using Multiple-Category Bayesian Models Trained on Chemogenomics Databases

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On Outliers and Activity CliffsWhy QSAR Often Disappoints

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Extended-Connectivity Fingerprints

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The Trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy)

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Predicting Kinase Selectivity Profiles Using Free-Wilson QSAR Analysis

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Classification of Kinase Inhibitors Using a Bayesian Model

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Structural Interaction Fingerprint (SIFt): A Novel Method for Analyzing Three-Dimensional Protein−Ligand Binding Interactions

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Trends in Kinase Selectivity: Insights for Target Class-Focused Library Screening

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Chemical Fragments as Foundations for Understanding Target Space and Activity Prediction

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Kinase Domain Mutations in Cancer: Implications for Small Molecule Drug Design Strategies

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Kinase Inhibitor Data Modeling and de Novo Inhibitor Design with Fragment Approaches

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Why QSAR Fails: An Empirical Evaluation Using Conventional Computational Approach

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The (un)targeted cancer kinome

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Targeting cancer with small molecule kinase inhibitors

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High-throughput kinase profiling as a platform for drug discovery

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An efficient rapid system for profiling the cellular activities of molecular libraries

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A systematic interaction map of validated kinase inhibitors with Ser/Thr kinases

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Statistical Learning Theory

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Protein-ligand interaction prediction: an improved chemogenomics approach

inferredcites →https://doi.org/10.1093/bioinformatics/btp650openalex-snapshot

Kinase selectivity potential for inhibitors targeting the ATP binding site: a network analysis

inferredcites →https://doi.org/10.1093/nar/gkl305openalex-snapshot

PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence

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SiMMap: a web server for inferring site-moiety map to recognize interaction preferences between protein pockets and compound moieties

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Structural Interaction Fingerprints: A New Approach to Organizing, Mining, Analyzing, and Designing Protein–Small Molecule Complexes

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CREDO: A Protein–Ligand Interaction Database for Drug Discovery

inferredcites →https://doi.org/10.1126/science.1075762openalex-snapshot

The Protein Kinase Complement of the Human Genome

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Clustering by Passing Messages Between Data Points

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LIBSVM

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Kinome-wide interaction modelling using alignment-based and alignment-independent approaches for kinase description and linear and non-linear data analysis techniques

inferredcites →https://doi.org/10.1186/1471-2105-8-438openalex-snapshot

Glycosylation site prediction using ensembles of Support Vector Machine classifiers

inferredcites →https://doi.org/10.1186/1471-2105-9-491openalex-snapshot

Prediction of specificity-determining residues for small-molecule kinase inhibitors

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Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond

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Bayesian Modeling in Virtual High Throughput Screening

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Strategies for the Design of Selective Protein Kinase Inhibitors

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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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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Additional file 1 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

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Additional file 2 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

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Additional file 3 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

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Additional file 4 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

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Additional file 5 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

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Additional file 6 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

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Additional file 7 of Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors

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Applications of proteochemometrics (PCM) : from species extrapolation to cell-line sensitivity modelling

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