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Structure Based Model for the Prediction of Phospholipidosis Induction Potential of Small Molecules

https://doi.org/10.1021/ci3001875
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What this work says

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

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Prediction of Cytochrome P450 Profiles of Environmental Chemicals with QSAR Models Built from Drug‐Like Molecules
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On Selection of Training and Test Sets for the Development of Predictive QSAR models
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The Nature of Statistical Learning Theory
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Lipidosis induced by amphiphilic cationic drugs
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Selective naphthalene H3 receptor inverse agonists with reduced potential to induce phospholipidosis and their quinoline analogs
inferredcites →https://doi.org/10.1016/j.febslet.2006.08.061openalex-snapshot
Drug‐induced phospholipidosis
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An introduction to ROC analysis
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Prediction of phospholipidosis-inducing potential of drugs by in vitro biochemical and physicochemical assays followed by multivariate analysis
inferredcites →https://doi.org/10.1021/ci030304fopenalex-snapshot
A Universal Molecular Descriptor System for Prediction of LogP, LogS, LogBB, and Absorption
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Prediction of Cytochrome P450 3A4, 2D6, and 2C9 Inhibitors and Substrates by Using Support Vector Machines
inferredcites →https://doi.org/10.1021/ci200028nopenalex-snapshot
Classification of Cytochrome P450 Inhibitors and Noninhibitors Using Combined Classifiers
inferredcites →https://doi.org/10.1021/ci200311wopenalex-snapshot
Predictive Models for Cytochrome P450 Isozymes Based on Quantitative High Throughput Screening Data
inferredcites →https://doi.org/10.1021/ci6004542openalex-snapshot
Evaluation of a Published in Silico Model and Construction of a Novel Bayesian Model for Predicting Phospholipidosis Inducing Potential
inferredcites →https://doi.org/10.1023/a:1020869118689openalex-snapshot
Predictive QSAR modeling based on diversity sampling of experimental datasets for the training and test set selection
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/10.1078/0940-2993-00338openalex-snapshot
Use of physicochemical calculation of pKa and CLogP to predict phospholipidosis-inducing potential
inferredcites →https://doi.org/10.1080/00401706.1999.10485951openalex-snapshot
Statistical Learning Theory
inferredcites →https://doi.org/10.1080/15376510701857262openalex-snapshot
Development of a Phospholipidosis Database and Predictive Quantitative Structure-Activity Relationship (QSAR) Models
inferredcites →https://doi.org/10.1089/adt.2007.119openalex-snapshot
Phospholipidosis Assay in HepG2 Cells and Rat or Rhesus Hepatocytes Using Phospholipid Probe NBD-PE
inferredcites →https://doi.org/10.1093/nar/26.4.879openalex-snapshot
Combinatorial library diversity: probability assessment of library populations
inferredcites →https://doi.org/10.1126/scitranslmed.3001862openalex-snapshot
The NCGC Pharmaceutical Collection: A Comprehensive Resource of Clinically Approved Drugs Enabling Repurposing and Chemical Genomics
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Editorial
inferredcites →https://doi.org/10.1293/tox.21.9openalex-snapshot
Drug-induced Phospholipidosis -Pathological Aspects and Its Prediction
inferredcites →https://doi.org/10.2131/jts.31.315openalex-snapshot
PHYSICOCHEMICAL AND CELL-BASED APPROACH FOR EARLY SCREENING OF PHOSPHOLIPIDOSIS-INDUCING POTENTIAL
inferredcites →https://doi.org/10.2174/092986709788803213openalex-snapshot
Medicinal Chemistry Strategies to Minimize Phospholipidosis
inferredcites →https://doi.org/10.2174/156802608786786589openalex-snapshot
Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential
inferredcites →https://doi.org/10.2174/1875397301004010057openalex-snapshot
A Grid Algorithm for High Throughput Fitting of Dose-Response Curve Data

What points at this work

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

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QSAR model based on weighted MCS trees approach for the representation of molecule data sets
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In Silico ADMET Profiling
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Computational identification of a phospholipidosis toxicophore using 13C and 15N NMR-distance based fingerprints
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A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
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An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
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Predicting the Risk of Phospholipidosis with in Silico Models and an Image-Based in Vitro Screen
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Modeling Phospholipidosis Induction: Reliability and Warnings
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How Does the Quality of Phospholipidosis Data Influence the Predictivity of Structural Alerts?
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Drug-induced Phospholipidosis
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Potentiation of Drug-Induced PhospholipidosisIn Vitrothrough PEGlyated Graphene Oxide as the Nanocarrier
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Advances in high-throughput screening technology for toxicology
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