On the public citation graph
Interpretable, Probability-Based Confidence Metric for Continuous Quantitative Structure–Activity Relationship Models
0 signed · 73 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.
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known work and none has been retracted — and get a dated CiteStamped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.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/jps.20217openalex-snapshot
Application of ALOGPS to predict 1‐octanol/water distribution coefficients, logP, and logD, of AstraZeneca in‐house database
inferredcites →https://doi.org/10.1002/minf.201200053openalex-snapshot
A Kernel‐Based Method for Assessing Uncertainty on Individual QSAR Predictions
inferredcites →https://doi.org/10.1002/qsar.200390007openalex-snapshot
The Importance of Being Earnest: Validation is the Absolute Essential for Successful Application and Interpretation of QSPR Models
inferredcites →https://doi.org/10.1016/j.chemolab.2006.04.013openalex-snapshot
Rejecting unclassifiable samples with decision forests
inferredcites →https://doi.org/10.1016/j.jmgm.2005.03.003openalex-snapshot
Assessing the reliability of a QSAR model's predictions
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/10.1016/s1093-3263(00)00068-1openalex-snapshot
A widely applicable set of descriptors
inferredcites →https://doi.org/10.1021/ci034143ropenalex-snapshot
Spline-Fitting with a Genetic Algorithm: A Method for Developing Classification Structure−Activity Relationships
inferredcites →https://doi.org/10.1021/ci049782wopenalex-snapshot
Similarity to Molecules in the Training Set Is a Good Discriminator for Prediction Accuracy in QSAR
inferredcites →https://doi.org/10.1021/ci0500381openalex-snapshot
A Stepwise Approach for Defining the Applicability Domain of SAR and QSAR Models
inferredcites →https://doi.org/10.1021/ci060117sopenalex-snapshot
On Outliers and Activity CliffsWhy QSAR Often Disappoints
inferredcites →https://doi.org/10.1021/ci100176xopenalex-snapshot
Trust, But Verify: On the Importance of Chemical Structure Curation in Cheminformatics and QSAR Modeling Research
inferredcites →https://doi.org/10.1021/ci300004nopenalex-snapshot
Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest
inferredcites →https://doi.org/10.1021/ci700332kopenalex-snapshot
The Trouble with QSAR (or How I Learned To Stop Worrying and Embrace Fallacy)
inferredcites →https://doi.org/10.1021/ci9000579openalex-snapshot
Predicting the Predictability: A Unified Approach to the Applicability Domain Problem of QSAR Models
inferredcites →https://doi.org/10.1021/ci900313uopenalex-snapshot
Chemical Domain of QSAR Models from Atom-Centered Fragments
inferredcites →https://doi.org/10.1021/ja01062a035openalex-snapshot
p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure
inferredcites →https://doi.org/10.1023/a:1025358319677openalex-snapshot
In silico ADME/Tox: why models fail
inferredcites →https://doi.org/10.1080/10629360902949567openalex-snapshot
How not to develop a quantitative structure–activity or structure–property relationship (QSAR/QSPR)
inferredcites →https://doi.org/10.1124/dmd.108.020479openalex-snapshot
Trend Analysis of a Database of Intravenous Pharmacokinetic Parameters in Humans for 670 Drug Compounds
inferredcites →https://doi.org/10.18637/jss.v028.i05openalex-snapshot
Building Predictive Models in R Using the caret Package
inferredcites →https://doi.org/10.18637/jss.v032.i05openalex-snapshot
Isotone Optimization in R : Pool-Adjacent-Violators Algorithm (PAVA) and Active Set Methods
inferredcites →https://doi.org/10.2174/092986709789578213openalex-snapshot
How to Recognize and Workaround Pitfalls in QSAR Studies: A Critical Review
inferredcites →https://doi.org/10.2174/138920008786485092openalex-snapshot
High Throughput ADME Screening: Practical Considerations, Impact on the Portfolio and Enabler of In Silico ADME Models
inferredcites →https://doi.org/10.2307/1402630openalex-snapshot
Statistical Inference under Order Restrictions (The Theory and Application of Isotonic Regression)
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/0471266949.bmc267openalex-snapshot
Artificial Intelligence in Medicinal Chemistry
inferred← citeshttps://doi.org/10.1002/9781118763070.ch3openalex-snapshot
QSAR modeling: prediction of biological activity from chemical structure
inferred← citeshttps://doi.org/10.1002/9781118783344.ch14openalex-snapshot
Integrated Assessment of Drug Clearance and Cross‐Species Scalability
inferred← citeshttps://doi.org/10.1002/9781118788523.ch20openalex-snapshot
Designing Peripheral Drugs for Minimal Brain Exposure
inferred← citeshttps://doi.org/10.1002/9781118819586.ch7openalex-snapshot
Experimental Screening Strategies to Reduce Attrition Risk
inferred← citeshttps://doi.org/10.1002/9783527840748.ch21openalex-snapshot
Advances in the Application of In Silico ADMET Models – An Industry Perspective
inferred← citeshttps://doi.org/10.1002/ffj.3430openalex-snapshot
Multivariate statistical analysis of a large odorants database aimed at revealing similarities and links between odorants and odors
inferred← citeshttps://doi.org/10.1007/978-1-0716-1787-8_4openalex-snapshot
Application of Artificial Intelligence and Machine Learning in Drug Discovery
inferred← citeshttps://doi.org/10.1007/978-3-319-23558-5_6openalex-snapshot
Quantitative-Structure Activity Relationship Modeling and Cheminformatics
inferred← citeshttps://doi.org/10.1007/978-3-319-50042-3_4openalex-snapshot
In Silico ADME Techniques Used in Early-Phase Drug Discovery
inferred← citeshttps://doi.org/10.1007/s10822-014-9719-1openalex-snapshot
Impact of distance-based metric learning on classification and visualization model performance and structure–activity landscapes
inferred← citeshttps://doi.org/10.1007/s11095-022-03205-1openalex-snapshot
In Vitro - in Vivo Extrapolation of Hepatic Clearance in Preclinical Species
inferred← citeshttps://doi.org/10.1016/b978-0-12-409547-2.12312-1openalex-snapshot
Drug Discovery Technologies: Current and Future Trends
inferred← citeshttps://doi.org/10.1016/j.bmcl.2014.09.069openalex-snapshot
Design, synthesis and evaluation of [3H]PF-7191, a highly specific nociceptin opioid peptide (NOP) receptor radiotracer for in vivo receptor occupancy (RO) studies
inferred← citeshttps://doi.org/10.1016/j.bmcl.2016.11.085openalex-snapshot
Amine promiscuity and toxicology analysis
inferred← citeshttps://doi.org/10.1016/j.ejmech.2019.111813openalex-snapshot
Structural attributes influencing unbound tissue distribution
inferred← citeshttps://doi.org/10.1016/j.fct.2017.09.037openalex-snapshot
Perspectives from the NanoSafety Modelling Cluster on the validation criteria for (Q)SAR models used in nanotechnology
inferred← citeshttps://doi.org/10.1016/j.isci.2022.104814openalex-snapshot
Uncertainty quantification: Can we trust artificial intelligence in drug discovery?
inferred← citeshttps://doi.org/10.1016/j.jfluchem.2016.12.013openalex-snapshot
Fluorine multipolar interaction: Toward elucidating its energetics in binding recognition
inferred← citeshttps://doi.org/10.1016/j.nexres.2026.102362openalex-snapshot
A comprehensive review of chemoinformatics: From molecular representation to data-driven design
inferred← citeshttps://doi.org/10.1016/j.xphs.2020.12.005openalex-snapshot
Evaluation of Prediction Accuracy for Volume of Distribution in Rat and Human Using In Vitro, In Vivo, PBPK and QSAR Methods
inferred← citeshttps://doi.org/10.1016/j.yrtph.2014.12.021openalex-snapshot
Introducing conformal prediction in predictive modeling for regulatory purposes. A transparent and flexible alternative to applicability domain determination
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.6b00037openalex-snapshot
Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.7b00048openalex-snapshot
Evaluating the Role of Multidrug Resistance Protein 3 (MDR3) Inhibition in Predicting Drug-Induced Liver Injury Using 125 Pharmaceuticals
inferred← citeshttps://doi.org/10.1021/acs.jcim.0c01164openalex-snapshot
Large-Scale Modeling of Multispecies Acute Toxicity End Points Using Consensus of Multitask Deep Learning Methods
inferred← citeshttps://doi.org/10.1021/acs.jcim.5b00110openalex-snapshot
The Relative Importance of Domain Applicability Metrics for Estimating Prediction Errors in QSAR Varies with Training Set Diversity
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00114openalex-snapshot
General Approach to Estimate Error Bars for Quantitative Structure–Activity Relationship Predictions of Molecular Activity
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00520openalex-snapshot
In-Silico Extraction of Design Ideas Using MMPA-by-QSAR and its Application on ADME Endpoints
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.6b00850openalex-snapshot
The Discovery of a Potent, Selective, and Peripherally Restricted Pan-Trk Inhibitor (PF-06273340) for the Treatment of Pain
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.7b00487openalex-snapshot
In Silico Absorption, Distribution, Metabolism, Excretion, and Pharmacokinetics (ADME-PK): Utility and Best Practices. An Industry Perspective from the International Consortium for Innovation through Quality in Pharmaceutical Development
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.7b01769openalex-snapshot
Identification of a Novel Positron Emission Tomography (PET) Ligand for Imaging β-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE-1) in Brain
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.8b00917openalex-snapshot
Dual Inhibition of TYK2 and JAK1 for the Treatment of Autoimmune Diseases: Discovery of (( S )-2,2-Difluorocyclopropyl)((1 R ,5 S )-3-(2-((1-methyl-1 H -pyrazol-4-yl)amino)pyrimidin-4-yl)-3,8-diazabicyclo[3.2.1]octan-8-yl)methanone (PF-06700841)
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.3c00502openalex-snapshot
The Comparison of Machine Learning and Mechanistic In Vitro–In Vivo Extrapolation Models for the Prediction of Human Intrinsic Clearance
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/ci5001168openalex-snapshot
Introducing Conformal Prediction in Predictive Modeling. A Transparent and Flexible Alternative to Applicability Domain Determination
inferred← citeshttps://doi.org/10.1021/ci500172zopenalex-snapshot
Applicability Domain Analysis (ADAN): A Robust Method for Assessing the Reliability of Drug Property Predictions
inferred← citeshttps://doi.org/10.1021/jm400312yopenalex-snapshot
Design and Selection Parameters to Accelerate the Discovery of Novel Central Nervous System Positron Emission Tomography (PET) Ligands and Their Application in the Development of a Novel Phosphodiesterase 2A PET Ligand
inferred← citeshttps://doi.org/10.1021/ml500479vopenalex-snapshot
Discovery of a Selective TRPM8 Antagonist with Clinical Efficacy in Cold-Related Pain
inferred← citeshttps://doi.org/10.1038/nrd4128openalex-snapshot
Chemical predictive modelling to improve compound quality
inferred← citeshttps://doi.org/10.1101/710251openalex-snapshot
Molecular Insights on exquisitely Selective SrtA inhibitors towards active site loop forming open/close lid conformations in SrtA from Bacillus anthracis
inferred← citeshttps://doi.org/10.1124/dmd.121.000497openalex-snapshot
Static and Dynamic Projections of Drug-Drug Interactions Caused by Cytochrome P450 3A Time-Dependent Inhibitors Measured in Human Liver Microsomes and Hepatocytes
inferred← citeshttps://doi.org/10.1124/dmd.124.001660openalex-snapshot
Projections of Drug-Drug Interactions Caused by Time-Dependent Inhibitors of Cytochrome P450 1A2, 2B6, 2C8, 2C9, 2C19, and 2D6 Using In Vitro Data in Static and Dynamic Models
inferred← citeshttps://doi.org/10.1186/1758-2946-6-34openalex-snapshot
Using beta binomials to estimate classification uncertainty for ensemble models
inferred← citeshttps://doi.org/10.1186/s13321-016-0164-0openalex-snapshot
An ensemble model of QSAR tools for regulatory risk assessment
inferred← citeshttps://doi.org/10.3390/molecules23112756openalex-snapshot
Study of the Applicability Domain of the QSAR Classification Models by Means of the Rivality and Modelability Indexes
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.