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Making Sense of Large-Scale Kinase Inhibitor Bioactivity Data Sets: A Comparative and Integrative Analysis

https://doi.org/10.1021/ci400709d
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What this work says

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

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Chemogenomic Approaches to Infer Drug–Target Interaction Networks
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The design, synthesis, and evaluation of 8 hybrid DFG-out allosteric kinase inhibitors: A structural analysis of the binding interactions of Gleevec®, Nexavar®, and BIRB-796
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Discovery of a potent and selective c-Kit inhibitor for the treatment of inflammatory diseases
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Transcriptional data: a new gateway to drug repositioning?
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c-Src Binds to the Cancer Drug Imatinib with an Inactive Abl/c-Kit Conformation and a Distributed Thermodynamic Penalty
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The Experimental Uncertainty of Heterogeneous Public Ki Data
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Large-scale prediction and testing of drug activity on side-effect targets
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Synergistic drug combinations tend to improve therapeutically relevant selectivity
inferredcites →https://doi.org/10.1038/nbt.1990openalex-snapshot
Comprehensive analysis of kinase inhibitor selectivity
inferredcites →https://doi.org/10.1038/nbt.2017openalex-snapshot
Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity
inferredcites →https://doi.org/10.1038/nbt.2021openalex-snapshot
Interrogating the kinome
inferredcites →https://doi.org/10.1038/nbt.2620openalex-snapshot
Chaperones as thermodynamic sensors of drug-target interactions reveal kinase inhibitor specificities in living cells
inferredcites →https://doi.org/10.1038/nbt0908-983openalex-snapshot
Data completeness—the Achilles heel of drug-target networks
inferredcites →https://doi.org/10.1038/nbt1338openalex-snapshot
Drug—target network
inferredcites →https://doi.org/10.1038/nbt1358openalex-snapshot
A quantitative analysis of kinase inhibitor selectivity
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Network pharmacology: the next paradigm in drug discovery
inferredcites →https://doi.org/10.1038/nchembio.530openalex-snapshot
Navigating the kinome
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Targeting the cancer kinome through polypharmacology
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A robustness-based approach to systems-oriented drug design
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Drug-target network in myocardial infarction reveals multiple side effects of unrelated drugs
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Structure and organization of drug-target networks: insights from genomic approaches for drug discovery
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Target identification of small molecules based on chemical biology approaches
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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
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A systematic interaction map of validated kinase inhibitors with Ser/Thr kinases
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Toward more realistic drug-target interaction predictions
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BRENDA, AMENDA and FRENDA the enzyme information system: new content and tools in 2009
inferredcites →https://doi.org/10.1093/nar/gkp253openalex-snapshot
IC50-to-Ki: a web-based tool for converting IC50 to Ki values for inhibitors of enzyme activity and ligand binding
inferredcites →https://doi.org/10.1093/nar/gkq1126openalex-snapshot
DrugBank 3.0: a comprehensive resource for 'Omics' research on drugs
inferredcites →https://doi.org/10.1093/nar/gkr1011openalex-snapshot
STITCH 3: zooming in on protein-chemical interactions
inferredcites →https://doi.org/10.1093/nar/gkr777openalex-snapshot
ChEMBL: a large-scale bioactivity database for drug discovery
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Therapeutic target database update 2012: a resource for facilitating target-oriented drug discovery
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SuperTarget goes quantitative: update on drug-target interactions
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KEGG for integration and interpretation of large-scale molecular data sets
inferredcites →https://doi.org/10.1126/scitranslmed.3002442openalex-snapshot
Activation of ERBB2 Signaling Causes Resistance to the EGFR-Directed Therapeutic Antibody Cetuximab
inferredcites →https://doi.org/10.1146/annurev-pharmtox-010611-134520openalex-snapshot
Systems Pharmacology: Network Analysis to Identify Multiscale Mechanisms of Drug Action
inferredcites →https://doi.org/10.1146/annurev-pharmtox-010611-134630openalex-snapshot
Novel Computational Approaches to Polypharmacology as a Means to Define Responses to Individual Drugs
inferredcites →https://doi.org/10.1186/1471-2105-12-77openalex-snapshot
pROC: an open-source package for R and S+ to analyze and compare ROC curves
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Drug repositioning for personalized medicine
inferredcites →https://doi.org/10.1371/journal.pcbi.1002333openalex-snapshot
Global Analysis of Small Molecule Binding to Related Protein Targets
inferredcites →https://doi.org/10.1371/journal.pcbi.1002503openalex-snapshot
Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference
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Chapter 7: Pharmacogenomics
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Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways
inferredcites →https://doi.org/10.1371/journal.pcbi.1003253openalex-snapshot
Target Prediction for an Open Access Set of Compounds Active against Mycobacterium tuberculosis
inferredcites →https://doi.org/10.1371/journal.pone.0016999openalex-snapshot
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inferredcites →https://doi.org/10.1371/journal.pone.0061007openalex-snapshot
Comparability of Mixed IC50 Data – A Statistical Analysis
inferredcites →https://doi.org/10.1371/journal.pone.0062975openalex-snapshot
A Semi-Supervised Method for Drug-Target Interaction Prediction with Consistency in Networks
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What points at this work

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

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Optimized differential evolution and hybrid deep learning for superior drug-target binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.aichem.2023.100020openalex-snapshot
Machine learning small molecule properties in drug discovery
inferred← citeshttps://doi.org/10.1016/j.ailsci.2023.100079openalex-snapshot
An industrial evaluation of proteochemometric modelling: Predicting drug-target affinities for kinases
inferred← citeshttps://doi.org/10.1016/j.ailsci.2024.100115openalex-snapshot
DTA Atlas: A massive-scale drug repurposing database
inferred← citeshttps://doi.org/10.1016/j.ailsci.2026.100160openalex-snapshot
CDTA: Consistency-based deep learning method for drug-target affinity prediction using sequential features from protein language model
inferred← citeshttps://doi.org/10.1016/j.ailsci.2026.100180openalex-snapshot
ISAAC: Prior-aligned structural sensitivity auditing for drug–target interaction models
inferred← citeshttps://doi.org/10.1016/j.apsb.2022.05.004openalex-snapshot
Kinome-wide polypharmacology profiling of small molecules by multi-task graph isomorphism network approach
inferred← citeshttps://doi.org/10.1016/j.apsb.2026.05.028openalex-snapshot
AI and machine learning for predicting in vitro/preclinical drug toxicities and adverse events in clinical patients
inferred← citeshttps://doi.org/10.1016/j.artmed.2024.102983openalex-snapshot
SSR-DTA: Substructure-aware multi-layer graph neural networks for drug–target binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.artmed.2024.102986openalex-snapshot
A comprehensive survey of drug–target interaction analysis in allopathy and siddha medicine
inferred← citeshttps://doi.org/10.1016/j.artmed.2025.103195openalex-snapshot
ContraDTI: Improved drug–target interaction prediction via multi-view contrastive learning
inferred← citeshttps://doi.org/10.1016/j.artmed.2026.103394openalex-snapshot
Twin cross contrastive learning with multi-modality fusion for drug–target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.asoc.2025.114376openalex-snapshot
GCDTA: Graph-attention-assisted contrastive learning for drug-target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.bbagen.2020.129545openalex-snapshot
Machine learning and ligand binding predictions: A review of data, methods, and obstacles
inferred← citeshttps://doi.org/10.1016/j.bbrc.2026.153292openalex-snapshot
TransGAT-DTA: A multi-task framework for drug–target affinity prediction and conditional molecule generation
inferred← citeshttps://doi.org/10.1016/j.biopha.2024.117688openalex-snapshot
Discovery of dual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) inhibitors using an artificial intelligence model and their effects on tau and tubulin dynamics
inferred← citeshttps://doi.org/10.1016/j.biopha.2025.118716openalex-snapshot
Drug target proteome profiling identifies HES1-driven mitotic catastrophe in ovarian serous carcinoma
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106110openalex-snapshot
GDilatedDTA: Graph dilation convolution strategy for drug target binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.bspc.2026.110899openalex-snapshot
VBMA-DTA: A multimodal drug–target binding affinity prediction framework via virtual bridging and multi-attention mechanism
inferred← citeshttps://doi.org/10.1016/j.chembiol.2016.05.016openalex-snapshot
Evidence-Based and Quantitative Prioritization of Tool Compounds in Phenotypic Drug Discovery
inferred← citeshttps://doi.org/10.1016/j.chembiol.2017.11.009openalex-snapshot
Drug Target Commons: A Community Effort to Build a Consensus Knowledge Base for Drug-Target Interactions
inferred← citeshttps://doi.org/10.1016/j.chemolab.2021.104405openalex-snapshot
Using drug-drug and protein-protein similarities as feature vector for drug-target binding prediction
inferred← citeshttps://doi.org/10.1016/j.chemolab.2025.105317openalex-snapshot
GCNFG-DTA:Screening natural medicinal components of Cyperus esculentus targeting kinases with AIDD methods
inferred← citeshttps://doi.org/10.1016/j.chemolab.2025.105601openalex-snapshot
A semantic framework for drug-target affinity prediction using Mamba and graph convolutional networks for multimodal feature fusion
inferred← citeshttps://doi.org/10.1016/j.cjprs.2022.10.004openalex-snapshot
DeepPurpose-based drug discovery in chondrosarcoma
inferred← citeshttps://doi.org/10.1016/j.cmpb.2023.108003openalex-snapshot
TransVAE-DTA: Transformer and variational autoencoder network for drug-target binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.cmpb.2024.108524openalex-snapshot
Bio-K-Transformer: A pre-trained transformer-based sequence-to-sequence model for adverse drug reactions prediction
inferred← citeshttps://doi.org/10.1016/j.cmpb.2025.109163openalex-snapshot
MDM-DTA: Message Passing Neural Network with molecular descriptors and Mixture of Experts for drug–target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2021.107529openalex-snapshot
Ensembling machine learning models to boost molecular affinity prediction
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2023.107968openalex-snapshot
Multi-dimensional search for drug–target interaction prediction by preserving the consistency of attention distribution
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2023.107969openalex-snapshot
ResBiGAAT: Residual Bi-GRU with attention for protein-ligand binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2023.107971openalex-snapshot
AffinityVAE: A multi-objective model for protein-ligand affinity prediction and drug design
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2023.107982openalex-snapshot
GTAMP-DTA: Graph transformer combined with attention mechanism for drug-target binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2024.108310openalex-snapshot
Improving binding affinity prediction by emphasizing local features of drug and protein
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2025.108416openalex-snapshot
PocketDTA: A pocket-based multimodal deep learning model for drug–target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2025.108472openalex-snapshot
DICCA-DTA: Diffusion and Contextualized Capsule Attention guided Factorized Cross-Pooling for Drug-Target Affinity prediction
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2025.108862openalex-snapshot
Efficient drug–target affinity prediction via interaction features and parallel CNN–BiLSTM with attention
inferred← citeshttps://doi.org/10.1016/j.compbiolchem.2026.109264openalex-snapshot
Deep learning-based prediction of drug–target interactions between antiviral drugs and SARS-CoV-2 proteins using an image-based representation approach
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2021.105049openalex-snapshot
Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106145openalex-snapshot
GSAML-DTA: An interpretable drug-target binding affinity prediction model based on graph neural networks with self-attention mechanism and mutual information
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.106722openalex-snapshot
Establishment of extensive artificial intelligence models for kinase inhibitor prediction: Identification of novel PDGFRB inhibitors
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107131openalex-snapshot
StackCPA: A stacking model for compound-protein binding affinity prediction based on pocket multi-scale features
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107136openalex-snapshot
A survey of drug-target interaction and affinity prediction methods via graph neural networks
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107512openalex-snapshot
GPCNDTA: Prediction of drug-target binding affinity through cross-attention networks augmented with graph features and pharmacophores
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108127openalex-snapshot
Predicting Drug–Protein Interactions through Branch-Chain Mining and multi-dimensional attention network
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108181openalex-snapshot
Multi-party collaborative drug discovery via federated learning
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108339openalex-snapshot
GraphormerDTI: A graph transformer-based approach for drug-target interaction prediction
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108376openalex-snapshot
G-K BertDTA: A graph representation learning and semantic embedding-based framework for drug-target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108435openalex-snapshot
Prediction of drug-target binding affinity based on deep learning models
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108699openalex-snapshot
Prediction of drug–target binding affinity based on multi-scale feature fusion
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.109927openalex-snapshot
KNU-DTI: KNowledge United Drug-Target Interaction prediction
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110438openalex-snapshot
Drug-target interaction/affinity prediction: Deep learning models and advances review
inferred← citeshttps://doi.org/10.1016/j.cplett.2026.142739openalex-snapshot
DCMP: A Double Cross-Graph Message Passing neural network with early fusion for protein-ligand binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.crmeth.2025.101184openalex-snapshot
In silico methods for drug-target interaction prediction
inferred← citeshttps://doi.org/10.1016/j.csbj.2020.03.025openalex-snapshot
Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model
inferred← citeshttps://doi.org/10.1016/j.csbj.2021.03.004openalex-snapshot
A review on compound-protein interaction prediction methods: Data, format, representation and model
inferred← citeshttps://doi.org/10.1016/j.csbj.2025.09.023openalex-snapshot
DrugForm-DTA: Towards real-world drug-target binding affinity model
inferred← citeshttps://doi.org/10.1016/j.drudis.2016.03.001openalex-snapshot
Transcriptional response networks for elucidating mechanisms of action of multitargeted agents
inferred← citeshttps://doi.org/10.1016/j.drudis.2022.02.023openalex-snapshot
Compound–protein interaction prediction by deep learning: Databases, descriptors and models
inferred← citeshttps://doi.org/10.1016/j.ejmech.2024.116735openalex-snapshot
Progress in the application of artificial intelligence in molecular generation models based on protein structure
inferred← citeshttps://doi.org/10.1016/j.ejmech.2025.117825openalex-snapshot
How generative Artificial Intelligence can transform drug discovery?
inferred← citeshttps://doi.org/10.1016/j.ejmech.2026.118840openalex-snapshot
An artificial intelligence model for accurate drug-target affinity prediction in medicinal chemistry
inferred← citeshttps://doi.org/10.1016/j.ejmech.2026.119301openalex-snapshot
MLI-DTA: An interpretable multimodal framework with multi-level interactions for drug-target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.112303openalex-snapshot
JSSM-DTA: Joint Sequence-Structure Modeling with multi-scale transformers for explainable Drug-Target Affinity prediction
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115525openalex-snapshot
EA-based hyperparameter optimization of hybrid deep learning models for effective drug-target interactions prediction
inferred← citeshttps://doi.org/10.1016/j.eswa.2021.115810openalex-snapshot
Drug-target continuous binding affinity prediction using multiple sources of information
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121274openalex-snapshot
GINCM-DTA: A graph isomorphic network with protein contact map representation for potential use against COVID-19 and Omicron subvariants BQ.1, BQ.1.1, XBB.1.5, XBB.1.16
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.121882openalex-snapshot
IMAEN: An interpretable molecular augmentation model for drug–target interaction prediction
inferred← citeshttps://doi.org/10.1016/j.eswa.2023.122334openalex-snapshot
TAG-DTA: Binding-region-guided strategy to predict drug-target affinity using transformers
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124647openalex-snapshot
Drug–target binding affinity prediction model based on multi-scale diffusion and interactive learning
inferred← citeshttps://doi.org/10.1016/j.eswa.2024.124873openalex-snapshot
PHCDTI: A multichannel parallel high-order feature crossover model for DTIs prediction
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.130918openalex-snapshot
MFCLDTA: Multi-scale feature contrastive learning for predicting drug-target binding affinity
inferred← citeshttps://doi.org/10.1016/j.eswa.2025.130985openalex-snapshot
Molecular mechanics-aware feature fusion framework for predicting protein-ligand binding affinity
inferred← citeshttps://doi.org/10.1016/j.fmre.2024.02.011openalex-snapshot
A review of deep learning methods for ligand based drug virtual screening
inferred← citeshttps://doi.org/10.1016/j.heliyon.2025.e42476openalex-snapshot
InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks
inferred← citeshttps://doi.org/10.1016/j.ijbiomac.2025.142871openalex-snapshot
Enhanced inhibitor–kinase affinity prediction via integrated multimodal analysis of drug molecule and protein sequence features
inferred← citeshttps://doi.org/10.1016/j.ijbiomac.2025.146324openalex-snapshot
Affinity prediction of inhibitor-kinase based on mixture of experts enhanced by multimodal feature semantic analysis
inferred← citeshttps://doi.org/10.1016/j.imu.2024.101604openalex-snapshot
WAE-DTI: Ensemble-based architecture for drug–target interaction prediction using descriptors and embeddings
inferred← citeshttps://doi.org/10.1016/j.ins.2022.09.043openalex-snapshot
Hierarchical graph representation learning for the prediction of drug-target binding affinity
inferred← citeshttps://doi.org/10.1016/j.ins.2024.121135openalex-snapshot
Effective drug-target affinity prediction via generative active learning
inferred← citeshttps://doi.org/10.1016/j.isci.2023.107646openalex-snapshot
Overfit deep neural network for predicting drug-target interactions
inferred← citeshttps://doi.org/10.1016/j.isci.2026.117576openalex-snapshot
Multiscale cross modal fusion and cascaded signal propagation for drug target interaction prediction
inferred← citeshttps://doi.org/10.1016/j.jbi.2020.103547openalex-snapshot
Multi-view self-attention for interpretable drug–target interaction prediction
inferred← citeshttps://doi.org/10.1016/j.jbi.2025.104926openalex-snapshot
MF-DTA: Predicting drug–target affinity with multi-modal feature fusion model
inferred← citeshttps://doi.org/10.1016/j.jep.2025.120451openalex-snapshot
MMFi-DPBML: Multi-molecular fingerprint feature fusion for predicting ingredient-target interactions in traditional Chinese medicine
inferred← citeshttps://doi.org/10.1016/j.jmb.2024.168843openalex-snapshot
DTA-GTOmega: Enhancing Drug-Target Binding Affinity Prediction with Graph Transformers Using OmegaFold Protein Structures
inferred← citeshttps://doi.org/10.1016/j.jmgm.2026.109429openalex-snapshot
Capturing induced-fit effects: A geometry-aware and interpretable framework for robust drug–target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.jocs.2025.102706openalex-snapshot
CKDTA: A chemical knowledge-enhanced framework for drug–target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.jocs.2026.102863openalex-snapshot
MCAL-DTA: A multimodal method integrating cross-attention and local feature enhancement for drug-target affinity prediction
inferred← citeshttps://doi.org/10.1016/j.jpha.2025.101508openalex-snapshot
Intelligence on the graph: Graph neural networks for mechanistic drug target discovery
inferred← citeshttps://doi.org/10.1016/j.knosys.2023.111195openalex-snapshot
Multi-perspective neural network for dual drug repurposing in Alzheimer’s disease
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.113699openalex-snapshot
MSN-DTA: A multi-scale node adaptive graph neural network for interpretable drug-target binding affinity prediction
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114063openalex-snapshot
DACMF-DTI: Dual attention embedded cross-modality fusion for drug-target interaction prediction
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.115587openalex-snapshot
Zero-shot learning with subsequence reordering pretraining for compound-protein interaction
inferred← citeshttps://doi.org/10.1016/j.knosys.2026.116696openalex-snapshot
SymfuseX: A framework bridging drug–target predictive modeling and target-specific molecular design via a symbiotic fusion mechanism
inferred← citeshttps://doi.org/10.1016/j.lddd.2026.100556openalex-snapshot
Evolutionary shifts in representation learning for DTI prediction from sequences to structures
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