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Profile-QSAR: A Novel meta-QSAR Method that Combines Activities across the Kinase Family To Accurately Predict Affinity, Selectivity, and Cellular Activity

https://doi.org/10.1021/ci1005004
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/0470037237.ch16openalex-snapshot
Success Stories of Computer‐Aided Design
inferredcites →https://doi.org/10.1016/1074-5521(95)90183-3openalex-snapshot
Tyrosine kinases: modular signaling enzymes with tunable specificities
inferredcites →https://doi.org/10.1016/1074-5521(95)90283-xopenalex-snapshot
Predicting ligand binding to proteins by affinity fingerprinting
inferredcites →https://doi.org/10.1016/j.bbapap.2003.11.028openalex-snapshot
Kinomics—structural biology and chemogenomics of kinase inhibitors and targets
inferredcites →https://doi.org/10.1016/j.drudis.2006.08.013openalex-snapshot
Chemogenomics: structuring the drug discovery process to gene families
inferredcites →https://doi.org/10.1016/j.tips.2009.07.004openalex-snapshot
Conciliating binding efficiency and polypharmacology
inferredcites →https://doi.org/10.1016/s0026-895x(25)15641-1openalex-snapshot
Modeling the Cannabinoid Receptor: A Three-Dimensional Quantitative Structure-Activity Analysis
inferredcites →https://doi.org/10.1016/s0167-4838(00)00262-4openalex-snapshot
Inhibitors of dihydrodipicolinate reductase, a key enzyme of the diaminopimelate pathway of Mycobacterium tuberculosis
inferredcites →https://doi.org/10.1016/s1359-6446(02)02468-6openalex-snapshot
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inferredcites →https://doi.org/10.1016/s1359-6446(05)03477-xopenalex-snapshot
Kinomics: characterizing the therapeutically validated kinase space
inferredcites →https://doi.org/10.1016/s1367-5931(00)00229-5openalex-snapshot
Chemogenomic approaches to drug discovery
inferredcites →https://doi.org/10.1021/ci020267copenalex-snapshot
Selecting Screening Candidates for Kinase and G Protein-Coupled Receptor Targets Using Neural Networks
inferredcites →https://doi.org/10.1021/ci600197yopenalex-snapshot
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inferredcites →https://doi.org/10.1021/ci7004548openalex-snapshot
AutoShim: Empirically Corrected Scoring Functions for Quantitative Docking with a Crystal Structure and IC50 Training Data
inferredcites →https://doi.org/10.1021/ci700455uopenalex-snapshot
Surrogate AutoShim: Predocking into a Universal Ensemble Kinase Receptor for Three Dimensional Activity Prediction, Very Quickly, without a Crystal Structure
inferredcites →https://doi.org/10.1021/ci900176yopenalex-snapshot
QSAR Models for Predicting the Similarity in Binding Profiles for Pairs of Protein Kinases and the Variation of Models between Experimental Data Sets
inferredcites →https://doi.org/10.1021/ci980105+openalex-snapshot
Bioactive Diversity and Screening Library Selection via Affinity Fingerprinting
inferredcites →https://doi.org/10.1021/ci990439eopenalex-snapshot
Flexsim-X: A Method for the Detection of Molecules with Similar Biological Activity
inferredcites →https://doi.org/10.1021/jm00077a003openalex-snapshot
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inferredcites →https://doi.org/10.1021/jm010253iopenalex-snapshot
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inferredcites →https://doi.org/10.1021/jm010548wopenalex-snapshot
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inferredcites →https://doi.org/10.1021/jm0300781openalex-snapshot
Performance of 3D-Database Molecular Docking Studies into Homology Models
inferredcites →https://doi.org/10.1021/jm0303195openalex-snapshot
Classification of Kinase Inhibitors Using a Bayesian Model
inferredcites →https://doi.org/10.1021/jm050362nopenalex-snapshot
A Critical Assessment of Docking Programs and Scoring Functions
inferredcites →https://doi.org/10.1021/jm060163jopenalex-snapshot
RETRACTED: A Priori Inference of Cross Reactivity for Drug-Targeted Kinases
inferredcites →https://doi.org/10.1021/jm101195aopenalex-snapshot
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inferredcites →https://doi.org/10.1021/jm701021bopenalex-snapshot
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inferredcites →https://doi.org/10.1021/jm701399fopenalex-snapshot
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inferredcites →https://doi.org/10.1021/jm901147eopenalex-snapshot
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inferredcites →https://doi.org/10.1038/nature09626openalex-snapshot
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inferredcites →https://doi.org/10.1038/nbt1284openalex-snapshot
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inferredcites →https://doi.org/10.1038/nrd1086openalex-snapshot
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inferredcites →https://doi.org/10.1038/nrg1317openalex-snapshot
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inferredcites →https://doi.org/10.1056/nejmcibr1013704openalex-snapshot
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inferredcites →https://doi.org/10.1073/pnas.0909299107openalex-snapshot
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inferredcites →https://doi.org/10.1126/science.1075762openalex-snapshot
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inferredcites →https://doi.org/10.1517/17460440802536496openalex-snapshot
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inferredcites →https://doi.org/10.1517/17460441.3.9.1011openalex-snapshot
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inferredcites →https://doi.org/10.2174/0929867043455684openalex-snapshot
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inferredcites →https://doi.org/10.2174/092986706777452452openalex-snapshot
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inferredcites →https://doi.org/10.2174/092986708783330683openalex-snapshot
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inferredcites →https://doi.org/10.2174/1389557043487367openalex-snapshot
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inferredcites →https://doi.org/10.2174/1568026053828394openalex-snapshot
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inferredcites →https://doi.org/10.2174/156802607780906753openalex-snapshot
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What points at this work

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

inferred← citeshttps://doi.org/10.1002/9783527818242.ch10openalex-snapshot
Computational Approaches for Target Inference
inferred← citeshttps://doi.org/10.1002/minf.201200002openalex-snapshot
Large‐Scale QSAR in Target Prediction and Phenotypic HTS Assessment
inferred← citeshttps://doi.org/10.1002/minf.201300091openalex-snapshot
Kinase‐Kernel Models: Accurate Chemogenomic Method for the Entire Human Kinome
inferred← citeshttps://doi.org/10.1002/minf.201700119openalex-snapshot
Protocols for the Design of Kinase‐focused Compound Libraries
inferred← citeshttps://doi.org/10.1002/wcms.1187openalex-snapshot
Modern 2D QSAR for drug discovery
inferred← citeshttps://doi.org/10.1007/978-3-030-23873-5_20openalex-snapshot
Artificial Intelligence in Biological Activity Prediction
inferred← citeshttps://doi.org/10.1007/978-3-030-30642-7_36openalex-snapshot
A Convolutional Neural Network for Virtual Screening of Molecular Fingerprints
inferred← citeshttps://doi.org/10.1007/s00894-018-3921-3openalex-snapshot
How to design potent and selective DYRK1B inhibitors? Molecular modeling study
inferred← citeshttps://doi.org/10.1007/s10822-012-9625-3openalex-snapshot
Biomacromolecular quantitative structure–activity relationship (BioQSAR): a proof-of-concept study on the modeling, prediction and interpretation of protein–protein binding affinity
inferred← citeshttps://doi.org/10.1007/s10822-013-9646-6openalex-snapshot
Cheminformatics aspects of high throughput screening: from robots to models: symposium summary
inferred← citeshttps://doi.org/10.1007/s10822-025-00734-wopenalex-snapshot
Design of novel PI3Kα and PI3Kγ inhibitors for cancer treatment using pharmacophore, protein–ligand contacts, and machine learning methods
inferred← citeshttps://doi.org/10.1007/s10822-026-00761-1openalex-snapshot
Comparing massively-multitask regression algorithms for drug discovery
inferred← citeshttps://doi.org/10.1016/bs.armc.2017.08.001openalex-snapshot
Kinase-Centric Computational Drug Development
inferred← citeshttps://doi.org/10.1016/j.ailsci.2024.100100openalex-snapshot
Machine learning proteochemometric models for Cereblon glue activity predictions
inferred← citeshttps://doi.org/10.1016/j.bbapap.2015.03.014openalex-snapshot
Perspective on computational and structural aspects of kinase discovery from IPK2014
inferred← citeshttps://doi.org/10.1016/j.clinthera.2013.08.005openalex-snapshot
Discovery of Novel Drugs for Promising Targets
inferred← citeshttps://doi.org/10.1016/j.cplett.2016.12.020openalex-snapshot
Spontaneous assembly of HSP90 inhibitors at water/octanol interface: A molecular dynamics simulation study
inferred← citeshttps://doi.org/10.1016/j.drudis.2023.103796openalex-snapshot
Artificial intelligence methods in kinase target profiling: Advances and challenges
inferred← citeshttps://doi.org/10.1016/j.ejmech.2020.112641openalex-snapshot
Identifying representative kinases for inhibitor evaluation via systematic analysis of compound-based target relationships
inferred← citeshttps://doi.org/10.1016/j.ejmech.2024.116413openalex-snapshot
Data-oriented protein kinase drug discovery
inferred← citeshttps://doi.org/10.1016/j.pt.2023.01.010openalex-snapshot
Drug discovery for parasitic diseases: powered by technology, enabled by pharmacology, informed by clinical science
inferred← citeshttps://doi.org/10.1016/j.tips.2012.03.005openalex-snapshot
Structural approaches to obtain kinase selectivity
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c00250openalex-snapshot
Using Predicted Bioactivity Profiles to Improve Predictive Modeling
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01342openalex-snapshot
Collaborative Profile-QSAR: A Natural Platform for Building Collaborative Models among Competing Companies
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00889openalex-snapshot
Active Site Sequence Representations of Human Kinases Outperform Full Sequence Representations for Affinity Prediction and Inhibitor Generation: 3D Effects in a 1D Model
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01417openalex-snapshot
HyperPCM: Robust Task-Conditioned Modeling of Drug–Target Interactions
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00595openalex-snapshot
The Development and Application of KinomePro-DL: A Deep Learning Based Online Small Molecule Kinome Selectivity Profiling Prediction Platform
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00059openalex-snapshot
QComp: A QSAR-Based Imputation Framework for Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02250openalex-snapshot
ProfhEX: Empowering Early Drug Discovery with Machine Learning-Based Target Profiling and Liability Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00520openalex-snapshot
Prediction of Protein Kinase–Ligand Interactions through 2.5D Kinochemometrics
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00737openalex-snapshot
Multi-Assay-Based Compound Prioritization via Assistance Utilization: A Machine Learning Framework
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00166openalex-snapshot
Profile-QSAR 2.0: Kinase Virtual Screening Accuracy Comparable to Four-Concentration IC50s for Realistically Novel Compounds
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00222openalex-snapshot
Determination of a Focused Mini Kinase Panel for Early Identification of Selective Kinase Inhibitors
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00768openalex-snapshot
Imputation of Assay Bioactivity Data Using Deep Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00375openalex-snapshot
All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration IC 50 s for 8558 Novartis Assays
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00836openalex-snapshot
The Metabolic Rainbow: Deep Learning Phase I Metabolism in Five Colors
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.0c01332openalex-snapshot
Evolution of Novartis’ Small Molecule Screening Deck Design
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.1c01789openalex-snapshot
Explainable Machine Learning for Property Predictions in Compound Optimization
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.6b00052openalex-snapshot
Toward the Validation of Maternal Embryonic Leucine Zipper Kinase: Discovery, Optimization of Highly Potent and Selective Inhibitors, and Preliminary Biology Insight
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.6b01611openalex-snapshot
Profiling Prediction of Kinase Inhibitors: Toward the Virtual Assay
inferred← citeshttps://doi.org/10.1021/acsmedchemlett.7b00258openalex-snapshot
Identification of Potent and Selective RIPK2 Inhibitors for the Treatment of Inflammatory Diseases
inferred← citeshttps://doi.org/10.1021/ci200314jopenalex-snapshot
Kinase-Kernel Models: Accurate In silico Screening of 4 Million Compounds Across the Entire Human Kinome
inferred← citeshttps://doi.org/10.1021/ci300059dopenalex-snapshot
Profile-QSAR and Surrogate AutoShim Protein-Family Modeling of Proteases
inferred← citeshttps://doi.org/10.1021/ci400065eopenalex-snapshot
Feasibility of Using Molecular Docking-Based Virtual Screening for Searching Dual Target Kinase Inhibitors
inferred← citeshttps://doi.org/10.1021/jacs.9b08660openalex-snapshot
Data-Driven Construction of Antitumor Agents with Controlled Polypharmacology
inferred← citeshttps://doi.org/10.1021/jm300452popenalex-snapshot
Discovery of Selective LRRK2 Inhibitors Guided by Computational Analysis and Molecular Modeling
inferred← citeshttps://doi.org/10.1021/jm400798jopenalex-snapshot
Selectivity Data: Assessment, Predictions, Concordance, and Implications
inferred← citeshttps://doi.org/10.1039/d0cp03424jopenalex-snapshot
Molecular insight into the anion effect and free volume effect of CO 2 solubility in multivalent ionic liquids
inferred← citeshttps://doi.org/10.1039/d3lp00092copenalex-snapshot
Experimental and computational studies on the effects of C(2) methylation on the properties and gas separation performance of polyimide-ionene membranes
inferred← citeshttps://doi.org/10.1063/5.0123091openalex-snapshot
Artificial intelligence: Evolving role in drug design
inferred← citeshttps://doi.org/10.1097/cmr.0000000000000922openalex-snapshot
Recent advances of artificial intelligence in melanoma clinical practice
inferred← citeshttps://doi.org/10.1101/2022.07.10.499476openalex-snapshot
Machine learning approaches to quantitively predict selectivity of compounds against hDAC1 and hDAC6 isoforms
inferred← citeshttps://doi.org/10.1101/2025.01.09.632140openalex-snapshot
Transfer learning applied in predicting small molecule bioactivity
inferred← citeshttps://doi.org/10.1109/access.2020.3027828openalex-snapshot
Extension of pQSAR: Ensemble Model Generated by Random Forest and Partial Least Squares Regressions
inferred← citeshttps://doi.org/10.1142/s2737416524500121openalex-snapshot
Machine Learning Approaches to Predict the Selectivity of Compounds against HDAC1 and HDAC6
inferred← citeshttps://doi.org/10.1145/3709026.3709045openalex-snapshot
Advanced AI in Molecular Biology: Predicting Biological Activity from Molecular Descriptor
inferred← citeshttps://doi.org/10.1186/s12859-020-03645-9openalex-snapshot
Convolutional architectures for virtual screening
inferred← citeshttps://doi.org/10.1186/s13321-020-00444-5openalex-snapshot
QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction
inferred← citeshttps://doi.org/10.1186/s13321-023-00799-5openalex-snapshot
Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors
inferred← citeshttps://doi.org/10.1517/17460441.2016.1117070openalex-snapshot
An overview of molecular fingerprint similarity search in virtual screening
inferred← citeshttps://doi.org/10.2174/1568026622666220701091339openalex-snapshot
Machine Learning and Artificial Intelligence: A Paradigm Shift in Big Data-Driven Drug Design and Discovery
inferred← citeshttps://doi.org/10.3389/fchem.2018.00133openalex-snapshot
How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors
inferred← citeshttps://doi.org/10.3390/molecules23102520openalex-snapshot
Artificial Intelligence in Drug Design
inferred← citeshttps://doi.org/10.4155/fmc-2021-0077openalex-snapshot
Minimal Screening Requirements for Identifying Highly Promiscuous Kinase Inhibitors
inferred← citeshttps://doi.org/10.4155/fmc.13.202openalex-snapshot
Predictive in silico Off-Target Profiling in Drug Discovery
inferred← citeshttps://doi.org/10.48175/ijarsct-18075openalex-snapshot
Role of Artificial Intelligence in Pharmacy
inferred← citeshttps://doi.org/10.48550/arxiv.1406.1231openalex-snapshot
Multi-task Neural Networks for QSAR Predictions
inferred← citeshttps://doi.org/10.5012/bkcs.2013.34.9.2680openalex-snapshot
Descriptor-Based Profile Analysis of Kinase Inhibitors to Predict Inhibitory Activity and to Grasp Kinase Selectivity
inferred← citeshttps://doi.org/10.6026/97320630012172openalex-snapshot
Prediction of kinase-inhibitor binding affinity using energetic parameters
inferred← citeshttps://doi.org/10.6084/m9.figshare.12617895openalex-snapshot
Additional file 1 of QSAR-derived affinity fingerprints (part 2): modeling performance for potency prediction
inferred← citeshttps://doi.org/10.6084/m9.figshare.26674835openalex-snapshot
Additional file 1 of Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors
inferred← citeshttps://doi.org/10.6084/m9.figshare.26674838openalex-snapshot
Additional file 2 of Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors
inferred← citeshttps://doi.org/10.70675/552cca88zc6d5z490az8030zc6a50fe8942copenalex-snapshot
Développement de nouvelles approches protéo-chimiométriques appliquées à l'étude des interactions et de la sélectivité des inhibiteurs de kinases
inferred← citeshttps://doi.org/10.70675/a6af77b4z321dz4ab5z8b89zb2a887c44ddcopenalex-snapshot
Evaluation of the use of public toxicological data for chemical hazard prediction through computational methods
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