On the public citation graph

Predictive Models for Cytochrome P450 Isozymes Based on Quantitative High Throughput Screening Data

0 signed · 93 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

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

inferredcites →https://doi.org/10.1007/978-1-4757-2440-0openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/bf00994018openalex-snapshot
Support-Vector Networks
inferredcites →https://doi.org/10.1016/j.jmgm.2008.01.002openalex-snapshot
The importance of the domain of applicability in QSAR modeling
inferredcites →https://doi.org/10.1021/bi00187a009openalex-snapshot
Activation of CYP3A4: Evidence for the Simultaneous Binding of Two Substrates in a Cytochrome P450 Active Site
inferredcites →https://doi.org/10.1021/ci030304fopenalex-snapshot
A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption
inferredcites →https://doi.org/10.1021/ci0342876openalex-snapshot
SVM-Based Feature Selection for Characterization of Focused Compound Collections
inferredcites →https://doi.org/10.1021/jm101356popenalex-snapshot
Improvement in Aqueous Solubility in Small Molecule Drug Discovery Programs by Disruption of Molecular Planarity and Symmetry
inferredcites →https://doi.org/10.1038/nature01862openalex-snapshot
Crystal structure of human cytochrome P450 2C9 with bound warfarin
inferredcites →https://doi.org/10.1038/nbt.1581openalex-snapshot
Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries
inferredcites →https://doi.org/10.1038/nbt1206-1565openalex-snapshot
What is a support vector machine?
inferredcites →https://doi.org/10.1046/j.1365-2125.1999.00073.xopenalex-snapshot
CYP3A4 drug interactions: correlation of 10 in vitro probe substrates
inferredcites →https://doi.org/10.1073/pnas.0603236103openalex-snapshot
Structural basis for ligand promiscuity in cytochrome P450 3A4
inferredcites →https://doi.org/10.1073/pnas.0604348103openalex-snapshot
Quantitative high-throughput screening: A titration-based approach that efficiently identifies biological activities in large chemical libraries
inferredcites →https://doi.org/10.1074/jbc.m511232200openalex-snapshot
Crystal Structure of Human Cytochrome P450 2D6
inferredcites →https://doi.org/10.1074/jbc.m611692200openalex-snapshot
Adaptations for the Oxidation of Polycyclic Aromatic Hydrocarbons Exhibited by the Structure of Human P450 1A2
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1080/00498250903000255openalex-snapshot
Human P450s involved in drug metabolism and the use of structural modelling for understanding substrate selectivity and binding affinity
inferredcites →https://doi.org/10.1089/ars.2010.3109openalex-snapshot
Conformational Plasticity and Structure/Function Relationships in Cytochromes P450
inferredcites →https://doi.org/10.1126/science.1099736openalex-snapshot
Crystal Structures of Human Cytochrome P450 3A4 Bound to Metyrapone and Progesterone
inferredcites →https://doi.org/10.1126/science.286.5439.487openalex-snapshot
Pharmacogenomics: Translating Functional Genomics into Rational Therapeutics
inferredcites →https://doi.org/10.1136/bmj.320.7240.987openalex-snapshot
Science, medicine, and the future: Pharmacogenetics
inferredcites →https://doi.org/10.1177/1087057104274091openalex-snapshot
Development of CYP3A4 Inhibition Models: Comparisons of Machine-Learning Techniques and Molecular Descriptors
inferredcites →https://doi.org/10.1517/17425250903158940openalex-snapshot
QSAR of cytochrome inhibitors
inferredcites →https://doi.org/10.1517/17425255.2.4.629openalex-snapshot
Luminogenic cytochrome P450 assays
inferredcites →https://doi.org/10.2174/092986709787002727openalex-snapshot
Structure of Cytochrome P450s and Personalized Drug
inferredcites →https://doi.org/10.2174/092986709788803033openalex-snapshot
Medicinal Chemistry Strategies to Reduce CYP2D6 Inhibitory Activity of Lead Candidates
inferredcites →https://doi.org/10.2174/156802606778108915openalex-snapshot
Machine Learning Techniques for In Silico Modeling of Drug Metabolism
inferredcites →https://doi.org/10.2174/156802606778108951openalex-snapshot
Computational Models for Predicting Interactions with Cytochrome p450 Enzyme
inferredcites →https://doi.org/10.4155/fmc.10.229openalex-snapshot
Structural Features of Cytochromes P450 and Ligands that Affect Drug Metabolism as Revealed by X-Ray Crystallography and NMR

What points at this work

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

inferred← citeshttps://doi.org/10.1002/9780470921920.edm139openalex-snapshot
QSAR and Molecular Modeling Approaches for Prediction of Drug Metabolism
inferred← citeshttps://doi.org/10.1002/jat.4601openalex-snapshot
Evaluation of machine learning models for cytochrome P450 3A4, 2D6, and 2C9 inhibition
inferred← citeshttps://doi.org/10.1002/minf.201200065openalex-snapshot
Prediction of Cytochrome P450 Profiles of Environmental Chemicals with QSAR Models Built from Drug‐Like Molecules
inferred← citeshttps://doi.org/10.1002/minf.201600126openalex-snapshot
Prediction of hERG Liability – Using SVM Classification, Bootstrapping and Jackknifing
inferred← citeshttps://doi.org/10.1007/7355_2013_25openalex-snapshot
Tactics to Avoid Inhibition of Cytochrome P450s
inferred← citeshttps://doi.org/10.1007/s00204-024-03756-9openalex-snapshot
Harnessing machine learning to predict cytochrome P450 inhibition through molecular properties
inferred← citeshttps://doi.org/10.1007/s00894-014-2322-5openalex-snapshot
Industrial applications of in silico ADMET
inferred← citeshttps://doi.org/10.1007/s10822-020-00308-yopenalex-snapshot
Large-scale evaluation of cytochrome P450 2C9 mediated drug interaction potential with machine learning-based consensus modeling
inferred← citeshttps://doi.org/10.1007/s11095-015-1800-5openalex-snapshot
Predicting Mouse Liver Microsomal Stability with “Pruned” Machine Learning Models and Public Data
inferred← citeshttps://doi.org/10.1016/b978-0-08-100098-4.00007-7openalex-snapshot
In Silico ADMET Profiling
inferred← citeshttps://doi.org/10.1016/j.addr.2015.03.014openalex-snapshot
Recent progresses in the exploration of machine learning methods as in-silico ADME prediction tools
inferred← citeshttps://doi.org/10.1016/j.addr.2015.04.020openalex-snapshot
Prediction of cytochrome P450 mediated metabolism
inferred← citeshttps://doi.org/10.1016/j.bmc.2016.12.049openalex-snapshot
Highly predictive and interpretable models for PAMPA permeability
inferred← citeshttps://doi.org/10.1016/j.bmc.2021.116388openalex-snapshot
CYPlebrity: Machine learning models for the prediction of inhibitors of cytochrome P450 enzymes
inferred← citeshttps://doi.org/10.1016/j.cmpb.2014.08.009openalex-snapshot
Drug/nondrug classification using Support Vector Machines with various feature selection strategies
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.106177openalex-snapshot
A unified GCNN model for predicting CYP450 inhibitors by using graph convolutional neural networks with attention mechanism
inferred← citeshttps://doi.org/10.1016/j.dmpk.2019.11.006openalex-snapshot
Computational prediction of cytochrome P450 inhibition and induction
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.1c00078openalex-snapshot
In Silico Prediction of CYP2C8 Inhibition with Machine-Learning Methods
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.3c00065openalex-snapshot
Machine Learning Models to Predict Cytochrome P450 2B6 Inhibitors and Substrates
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.3c00305openalex-snapshot
Deep Learning Models Compared to Experimental Variability for the Prediction of CYP3A4 Time-Dependent Inhibition
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00628openalex-snapshot
iCYP-MFE: Identifying Human Cytochrome P450 Inhibitors Using Multitask Learning and Molecular Fingerprint-Embedded Encoding
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00123openalex-snapshot
Retro Drug Design: From Target Properties to Molecular Structures
inferred← citeshttps://doi.org/10.1021/acs.jcim.2c01516openalex-snapshot
Predictive Models for Human Cytochrome P450 3A7 Selective Inhibitors and Substrates
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01241openalex-snapshot
An Uncertainty-Guided Deep Learning Method Facilitates Rapid Screening of CYP3A4 Inhibitors
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01396openalex-snapshot
Prediction of Cytochrome P450 Inhibition Using a Deep Learning Approach and Substructure Pattern Recognition
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01735openalex-snapshot
Deep Learning of CYP450 Binding of Small Molecules by Quantum Information
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00130openalex-snapshot
Rule-Based Prediction Models of Cytochrome P450 Inhibition
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00143openalex-snapshot
Open Source Bayesian Models. 1. Application to ADME/Tox and Drug Discovery Datasets
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00469openalex-snapshot
Simulation of the Bottleneck Controlling Access into a Rieske Active Site: Predicting Substrates of Naphthalene 1,2-Dioxygenase
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00801openalex-snapshot
ADMET Evaluation in Drug Discovery. 19. Reliable Prediction of Human Cytochrome P450 Inhibition Using Artificial Intelligence Approaches
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.1c01564openalex-snapshot
Synthesis and Evaluation of Novel Tetrahydronaphthyridine CXCR4 Antagonists with Improved Drug-like Profiles
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.0c00637openalex-snapshot
Structure–Property Relationships and Machine Learning Models for Addressing CYP3A4-Mediated Victim Drug–Drug Interaction Risk in Drug Discovery
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.8b00110openalex-snapshot
Prediction of Human Cytochrome P450 Inhibition Using a Multitask Deep Autoencoder Neural Network
inferred← citeshttps://doi.org/10.1021/acsmedchemlett.7b00406openalex-snapshot
Synthesis of Novel Tetrahydroisoquinoline CXCR4 Antagonists with Rigidified Side-Chains
inferred← citeshttps://doi.org/10.1021/bk-2016-1222.ch014openalex-snapshot
Steps Toward a Virtual Rat: Predictive Absorption, Distribution, Metabolism, and Toxicity Models
inferred← citeshttps://doi.org/10.1021/ci200542mopenalex-snapshot
Computational Prediction of Metabolism: Sites, Products, SAR, P450 Enzyme Dynamics, and Mechanisms
inferred← citeshttps://doi.org/10.1021/ci300004nopenalex-snapshot
Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest
inferred← citeshttps://doi.org/10.1021/ci3001875openalex-snapshot
Structure Based Model for the Prediction of Phospholipidosis Induction Potential of Small Molecules
inferred← citeshttps://doi.org/10.1021/ci400482eopenalex-snapshot
Using Random Forest To Model the Domain Applicability of Another Random Forest Model
inferred← citeshttps://doi.org/10.1021/ci400694qopenalex-snapshot
Fuzzy Matched Pairs: A Means To Determine the Pharmacophore Impact on Molecular Interaction
inferred← citeshttps://doi.org/10.1038/srep42717openalex-snapshot
SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules
inferred← citeshttps://doi.org/10.1039/c5ra17196bopenalex-snapshot
An improved large-scale prediction model of CYP1A2 inhibitors by using combined fragment descriptors
inferred← citeshttps://doi.org/10.1080/1062936x.2017.1399925openalex-snapshot
In silico prediction of multiple-category classification model for cytochrome P450 inhibitors and non-inhibitors using machine-learning method
inferred← citeshttps://doi.org/10.1093/bioinformatics/btt325openalex-snapshot
WhichCyp: prediction of cytochromes P450 inhibition
inferred← citeshttps://doi.org/10.1093/bioinformatics/btv043openalex-snapshot
CypRules: a rule-based P450 inhibition prediction server
inferred← citeshttps://doi.org/10.1109/aimhc59811.2024.00029openalex-snapshot
Wasserstein Distance-Based Graph Kernel for Enhancing Drug Safety and Efficacy Prediction *
inferred← citeshttps://doi.org/10.1109/bibm.2013.6732515openalex-snapshot
Prediction of cytochrome P450 inhibition using ensemble of extreme learning machine
inferred← citeshttps://doi.org/10.1109/ojemb.2024.3480708openalex-snapshot
Optimal Transport Based Graph Kernels for Drug Property Prediction
inferred← citeshttps://doi.org/10.1109/wocc63563.2025.11082207openalex-snapshot
Identifying Human Cytochrome P450 Inhibitors Using Multimodal Data and Transformer Encoder
inferred← citeshttps://doi.org/10.1124/dmd.119.086504openalex-snapshot
Structure-Guided In Vitro to In Vivo Pharmacokinetic Optimization of Propargyl-Linked Antifolates
inferred← citeshttps://doi.org/10.1142/s146902681750002xopenalex-snapshot
Classification of Cytochrome P450 1A2 Inhibitors and Noninhibitors Based on Deep Belief Network
inferred← citeshttps://doi.org/10.1186/s13321-025-01015-2openalex-snapshot
Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning
inferred← citeshttps://doi.org/10.1208/s12248-012-9358-1openalex-snapshot
Paradigm Shift in Toxicity Testing and Modeling
inferred← citeshttps://doi.org/10.1504/ijram.2017.082562openalex-snapshot
Advances in high-throughput screening technology for toxicology
inferred← citeshttps://doi.org/10.1517/17460441.2014.866943openalex-snapshot
Support vector machines for drug discovery
inferred← citeshttps://doi.org/10.2174/1568009619666190320102238openalex-snapshot
Advances in In-Silico based Predictive In-Vivo Profiling of Novel Potent β-Glucuronidase Inhibitors
inferred← citeshttps://doi.org/10.2174/1573409914666181018141602openalex-snapshot
Virtual Screening Meets Deep Learning
inferred← citeshttps://doi.org/10.26686/wgtn.25044362openalex-snapshot
De novo Molecular Design using Deep Learning
inferred← citeshttps://doi.org/10.3389/fphar.2023.1099093openalex-snapshot
DEEPCYPs: A deep learning platform for enhanced cytochrome P450 activity prediction
inferred← citeshttps://doi.org/10.3390/ijms17060914openalex-snapshot
In Silico Prediction of Cytochrome P450-Drug Interaction: QSARs for CYP3A4 and CYP2C9
inferred← citeshttps://doi.org/10.3390/ijms23158498openalex-snapshot
Computer-Aided (In Silico) Modeling of Cytochrome P450-Mediated Food–Drug Interactions (FDI)
inferred← citeshttps://doi.org/10.48550/arxiv.2105.05220openalex-snapshot
Retro Drug Design: From Target Properties to Molecular Structures
inferred← citeshttps://doi.org/10.51415/10321/1751openalex-snapshot
Computational and micro-analytical techniques to study the in vitro and in silico models of novel therapeutic drugs
inferred← citeshttps://doi.org/10.82308/17726openalex-snapshot
From desktop to benchtop - Developing computational tools for organic and medicinal chemistry
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.