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Predicting Phospholipidosis Using Machine Learning

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What this work says

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

inferredcites →https://doi.org/10.1007/bf00994018openalex-snapshot
Support-Vector Networks
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Virtual Computational Chemistry Laboratory – Design and Description
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Comparison of the predicted and observed secondary structure of T4 phage lysozyme
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Lipidosis induced by amphiphilic cationic drugs
inferredcites →https://doi.org/10.1016/0006-2952(84)90286-7openalex-snapshot
Effect of cationic amphiphilic drugs on the hydrolysis of acidic and neutral phospholipids by liver lysosomal phospholipase A
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A review of the biology and toxicologic implications of the induction of lysosomal lamellar bodies by drugs
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Comparative evaluation of amiodarone-induced phospholipidosis and drug accumulation in Fischer-344 and Sprague-Dawley rats
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Drug design by machine learning: support vector machines for pharmaceutical data analysis
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SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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A Discussion of Measures of Enrichment in Virtual Screening: Comparing the Information Content of Descriptors with Increasing Levels of Sophistication
inferredcites →https://doi.org/10.1021/ci0601160openalex-snapshot
Chemoinformatics-Based Classification of Prohibited Substances Employed for Doping in Sport
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/10.1021/ci700157bopenalex-snapshot
y-Randomization and Its Variants in QSPR/QSAR
inferredcites →https://doi.org/10.1023/a:1010933404324openalex-snapshot
Random Forests
inferredcites →https://doi.org/10.1023/a:1012487302797openalex-snapshot
Gene Selection for Cancer Classification using Support Vector Machines
inferredcites →https://doi.org/10.1039/b209064nopenalex-snapshot
Managing molecular diversity
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/10.1080/15376510701857262openalex-snapshot
Development of a Phospholipidosis Database and Predictive Quantitative Structure-Activity Relationship (QSAR) Models
inferredcites →https://doi.org/10.1093/bioinformatics/16.5.412openalex-snapshot
Assessing the accuracy of prediction algorithms for classification: an overview
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A Toxicogenomic Approach to Drug-Induced Phospholipidosis: Analysis of Its Induction Mechanism and Establishment of a Novel in Vitro Screening System
inferredcites →https://doi.org/10.1093/toxsci/kfm157openalex-snapshot
In Vitro Detection of Drug-Induced Phospholipidosis Using Gene Expression and Fluorescent Phospholipid Based Methodologies
inferredcites →https://doi.org/10.1145/1656274.1656278openalex-snapshot
The WEKA data mining software
inferredcites →https://doi.org/10.1177/019262339702500111openalex-snapshot
Cationic Amphiphilic Drug-Induced Phospholipidosis
inferredcites →https://doi.org/10.1177/153537020122600903openalex-snapshot
Drug-Induced Phospholipidosis: Are There Functional Consequences?
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/10.2174/156802608786786589openalex-snapshot
Weka Machine Learning for Predicting the Phospholipidosis Inducing Potential

What points at this work

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

inferred← citeshttps://doi.org/10.1002/cmdc.201200306openalex-snapshot
Identification of Drugs Inducing Phospholipidosis by Novel in vitro Data
inferred← citeshttps://doi.org/10.1002/cmdc.201400027openalex-snapshot
Nonlinear Dimensionality Reduction for Visualizing Toxicity Data: Distance‐Based Versus Topology‐Based Approaches
inferred← citeshttps://doi.org/10.1002/minf.201200048openalex-snapshot
Construction and Consensus Performance of (Q)SAR Models for Predicting Phospholipidosis Using a Dataset of 743 Compounds
inferred← citeshttps://doi.org/10.1002/wcms.1183openalex-snapshot
Machine learning methods in chemoinformatics
inferred← citeshttps://doi.org/10.1007/7355_2013_34openalex-snapshot
Drug-Induced Phospholipidosis: Prediction, Detection, and Mitigation Strategies
inferred← citeshttps://doi.org/10.1007/978-3-030-75855-4_7openalex-snapshot
On the Study of Machine Learning Algorithms Towards Healthcare Applications
inferred← citeshttps://doi.org/10.1007/s00044-024-03260-wopenalex-snapshot
In silico drug discovery: a machine learning-driven systematic review
inferred← citeshttps://doi.org/10.1007/s00702-018-1923-zopenalex-snapshot
Derivatization of common antidepressant drugs increases inhibition of acid sphingomyelinase and reduces induction of phospholipidosis
inferred← citeshttps://doi.org/10.1007/s11426-013-4946-1openalex-snapshot
Classification of blocker and non-blocker of hERG potassium ion channel using a support vector machine
inferred← citeshttps://doi.org/10.1007/s12539-013-0147-zopenalex-snapshot
Docking and in silico ADMET studies of noraristeromycin, curcumin and its derivatives with Plasmodium falciparum SAH hydrolase: A molecular drug target against malaria
inferred← citeshttps://doi.org/10.1016/b978-0-12-386009-5.00001-1openalex-snapshot
Drug-Induced Phospholipidosis
inferred← citeshttps://doi.org/10.1016/b978-0-443-15177-4.00010-8openalex-snapshot
Sensors and model-based approaches applied for phospholipase activity detection
inferred← citeshttps://doi.org/10.1016/j.bbalip.2012.08.013openalex-snapshot
Drug induced phospholipidosis: An acquired lysosomal storage disorder
inferred← citeshttps://doi.org/10.1016/j.chembiol.2023.09.003openalex-snapshot
A machine learning and live-cell imaging tool kit uncovers small molecules induced phospholipidosis
inferred← citeshttps://doi.org/10.1016/j.comtox.2018.08.001openalex-snapshot
Introducing a computational method to estimate and prioritize systemic body exposure of organic chemicals in humans using their physicochemical properties
inferred← citeshttps://doi.org/10.1016/j.comtox.2022.100226openalex-snapshot
Development of an in silico consensus model for the prediction of the phospholipigenic potential of small molecules
inferred← citeshttps://doi.org/10.1016/j.drudis.2014.10.012openalex-snapshot
Machine-learning approaches in drug discovery: methods and applications
inferred← citeshttps://doi.org/10.1016/j.ejmech.2014.12.028openalex-snapshot
Evaluating the risk of phospholipidosis using a new multidisciplinary pipeline approach
inferred← citeshttps://doi.org/10.1016/j.ijinfomgt.2013.02.005openalex-snapshot
Towards model governance in predictive toxicology
inferred← citeshttps://doi.org/10.1016/j.jtbi.2019.07.009openalex-snapshot
Exploiting ensemble learning to improve prediction of phospholipidosis inducing potential
inferred← citeshttps://doi.org/10.1016/j.molstruc.2016.05.086openalex-snapshot
High throughput virtual screening and in silico ADMET analysis for rapid and efficient identification of potential PAP248-286 aggregation inhibitors as anti-HIV agents
inferred← citeshttps://doi.org/10.1016/j.patter.2025.101453openalex-snapshot
Experimental and machine learning-based exploration of repurposed drugs reveals chemical features underlying phospholipidosis
inferred← citeshttps://doi.org/10.1016/j.xphs.2024.05.002openalex-snapshot
Construction of Discrimination Models of Cationic Drugs for Phospholipidosis Induction Potential by Using Interaction Data with Immobilized Artificial Membrane as Well as Physicochemical Properties
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.9b00227openalex-snapshot
An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5c00596openalex-snapshot
Antibody-Drug Conjugates of NLRP3 Agonists: How Overcoming Lysosomal Accumulation Necessitated Noncanonical Linker Attachments
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.3c00964openalex-snapshot
AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis
inferred← citeshttps://doi.org/10.1021/acsomega.0c00684openalex-snapshot
Establishing GPCR Targets of hMAO Active Anthraquinones from Cassia obtusifolia Linn Seeds Using In Silico and In Vitro Methods
inferred← citeshttps://doi.org/10.1021/acsomega.0c03048openalex-snapshot
Comparison of Machine Learning Models on Performance of Single- and Dual-Type Electrochromic Devices
inferred← citeshttps://doi.org/10.1021/ci300254wopenalex-snapshot
Automating Knowledge Discovery for Toxicity Prediction Using Jumping Emerging Pattern Mining
inferred← citeshttps://doi.org/10.1021/ci400113topenalex-snapshot
Modeling Phospholipidosis Induction: Reliability and Warnings
inferred← citeshttps://doi.org/10.1021/ci500233kopenalex-snapshot
How Does the Quality of Phospholipidosis Data Influence the Predictivity of Structural Alerts?
inferred← citeshttps://doi.org/10.1021/mp3004385openalex-snapshot
Role of Electrostatic Potential in the in Silico Prediction of Molecular Bioactivation and Mutagenesis
inferred← citeshttps://doi.org/10.1039/9781788016414-00382openalex-snapshot
Drug-induced Phospholipidosis
inferred← citeshttps://doi.org/10.1101/2021.03.23.436648openalex-snapshot
Phospholipidosis is a shared mechanism underlying the in vitro antiviral activity of many repurposed drugs against SARS-CoV-2
inferred← citeshttps://doi.org/10.1126/science.abi4708openalex-snapshot
Drug-induced phospholipidosis confounds drug repurposing for SARS-CoV-2
inferred← citeshttps://doi.org/10.1177/0192623317749452openalex-snapshot
Characterizing Adversity of Lysosomal Accumulation in Nonclinical Toxicity Studies: Results from the 5th ESTP International Expert Workshop
inferred← citeshttps://doi.org/10.1186/1471-2105-13-60openalex-snapshot
Is EC class predictable from reaction mechanism?
inferred← citeshttps://doi.org/10.1186/1471-2210-12-1openalex-snapshot
Computational models for in-vitro anti-tubercular activity of molecules based on high-throughput chemical biology screening datasets
inferred← citeshttps://doi.org/10.1186/1756-0500-4-504openalex-snapshot
Predictive models for anti-tubercular molecules using machine learning on high-throughput biological screening datasets
inferred← citeshttps://doi.org/10.1186/1758-2946-4-2openalex-snapshot
Predicting the mechanism of phospholipidosis
inferred← citeshttps://doi.org/10.1371/journal.pone.0241543openalex-snapshot
Repurposing therapeutics for COVID-19: Rapid prediction of commercially available drugs through machine learning and docking
inferred← citeshttps://doi.org/10.14569/ijacsa.2017.081215openalex-snapshot
Machine Learning based Predictive Model for Screening Mycobacterium Tuberculosis Transcriptional Regulatory Protein Inhibitors from High-Throughput Screening Dataset
inferred← citeshttps://doi.org/10.2131/fts.11.279openalex-snapshot
Constructing a graph neural network-based artificial intelligence model to predict drug-induced phospholipidosis potential
inferred← citeshttps://doi.org/10.26434/chemrxiv.9524219openalex-snapshot
Discovery of Highly Polymorphic Organic Materials: A New Machine Learning Approach
inferred← citeshttps://doi.org/10.26434/chemrxiv.9524219.v1openalex-snapshot
Discovery of Highly Polymorphic Organic Materials: A New Machine Learning Approach
inferred← citeshttps://doi.org/10.31083/j.jin2201010openalex-snapshot
Bromophenols from Symphyocladia latiuscula (Harvey) Yamada as Novel Cholecystokinin 2 Receptor Antagonists
inferred← citeshttps://doi.org/10.3390/ijms27020999openalex-snapshot
N, N-Dimethyl-4-Aminopyridine- and Aluminum Isopropoxide-Catalysed Ring-Opening Polymerizations of β-Butyrolactone for the Antimicrobial Oligohydroxybutyrate
inferred← citeshttps://doi.org/10.4018/ijqspr.20200701.oa2openalex-snapshot
Modeling Binary Fingerprint Descriptors With the Superposing Significant Interaction Rules (SSIR) Method
inferred← citeshttps://doi.org/10.4155/bio-2020-0114openalex-snapshot
Qualified Method for the Estimation of di-18:1 Bis(Monoacylglycero)Phosphate in Urine, a Noninvasive Biomarker to Monitor Drug-Induced Phospholipidosis
inferred← citeshttps://doi.org/10.4155/fmc.11.11openalex-snapshot
Informatics, Machine Learning and Computational Medicinal Chemistry
inferred← citeshttps://doi.org/10.6026/97320630013154openalex-snapshot
QSAR based predictive modeling for anti-malarial molecules
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