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Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Molecular Descriptors for Chemoinformatics
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Large‐Scale Evaluation of log P Predictors: Local Corrections May Compensate Insufficient Accuracy and Need of Experimentally Testing Every Other Compound
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Applicability domain for in silico models to achieve accuracy of experimental measurements
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Partial least squares for discrimination
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Calculation of Molecular Lipophilicity: State-of-the-Art and Comparison of LogP Methods on more than 96,000 Compounds
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Associative Neural Network
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Measuring Intra-Assay Agreement for the Ames Salmonella Assay
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Data Mining: Practical Machine Learning Tools and Techniques
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inferredcites →https://doi.org/10.1021/ci900161gopenalex-snapshot
Benchmark Data Set for in Silico Prediction of Ames Mutagenicity
inferredcites →https://doi.org/10.1021/ci900203nopenalex-snapshot
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Gene Selection for Cancer Classification using Support Vector Machines
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Statistical Learning Theory
inferredcites →https://doi.org/10.1080/15287398809531194openalex-snapshot
Computer‐assisted analysis of interlaboratory Ames test variability
inferredcites →https://doi.org/10.1080/15376510701857353openalex-snapshot
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Individual Comparisons by Ranking Methods
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Cell Painting and Chemical Structure Read-Across Can Complement Each Other for Rat Acute Oral Toxicity Prediction in Chemical Early Derisking
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Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver Injury
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5b00481openalex-snapshot
ToxCast EPA in Vitro to in Vivo Challenge: Insight into the Rank-I Model
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5c00018openalex-snapshot
Consensus Modeling Strategies for Predicting Transthyretin Binding Affinity from Tox24 Challenge Data
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.5c00033openalex-snapshot
Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World
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.6c00065openalex-snapshot
Beyond Heuristics: A Model-Agnostic Framework for Uncertainty Quantification in QSAR via Adaptive Conformal Prediction
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.9b00259openalex-snapshot
Hierarchical Quantitative Structure–Activity Relationship Modeling Approach for Integrating Binary, Multiclass, and Regression Models of Acute Oral Systemic Toxicity
inferred← citeshttps://doi.org/10.1021/acs.chemrev.5b00215openalex-snapshot
A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes
inferred← citeshttps://doi.org/10.1021/acs.est.0c05522openalex-snapshot
Head, Shoulders, Knees, and Toes: Placement of Wearable Passive Samplers Alters Exposure Profiles Observed
inferred← citeshttps://doi.org/10.1021/acs.est.1c06481openalex-snapshot
Assessing the External Exposome Using Wearable Passive Samplers and High-Resolution Mass Spectrometry among South African Children Participating in the VHEMBE Study
inferred← citeshttps://doi.org/10.1021/acs.est.5c06790openalex-snapshot
In Silico Frontiers Shaping the Next Generation of Transformation Product Prediction and Toxicological Assessment
inferred← citeshttps://doi.org/10.1021/acs.est.5c18003openalex-snapshot
Reagent-Free Prediction of Free-Ammonia Toxicity in Algal Systems Using Chlorophyll Fluorescence Transients and Interpretable Sparse Regression
inferred← citeshttps://doi.org/10.1021/acs.est.5c18271openalex-snapshot
Structure-Informed Comprehensive Screening and Risk Prioritization of Organophosphorus Compounds in Human Serum: Revealing the Hidden Burden and Emerging Risks
inferred← citeshttps://doi.org/10.1021/acs.est.8b04056openalex-snapshot
Consensus Modeling of Median Chemical Intake for the U.S. Population Based on Predictions of Exposure Pathways
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.0c01294openalex-snapshot
GGL-Tox: Geometric Graph Learning for Toxicity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00451openalex-snapshot
ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities
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.5b00415openalex-snapshot
Harmonization of QSAR Best Practices and Molecular Docking Provides an Efficient Virtual Screening Tool for Discovering New G-Quadruplex Ligands
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00464openalex-snapshot
Upgrading Reliability in Molecular Property Prediction by Robust Quantification of Uncertainty from Machine Learning Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00698openalex-snapshot
PROFIS: Design of Target-Focused Libraries by Probing Continuous Fingerprint Space with Recurrent Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.6b00400openalex-snapshot
Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00159openalex-snapshot
Applying Mondrian Cross-Conformal Prediction To Estimate Prediction Confidence on Large Imbalanced Bioactivity Data Sets
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00274openalex-snapshot
Interpretation of Quantitative Structure–Activity Relationship Models: Past, Present, and Future
inferred← citeshttps://doi.org/10.1021/acs.jcim.7b00649openalex-snapshot
Discussion on Regression Methods Based on Ensemble Learning and Applicability Domains of Linear Submodels
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00054openalex-snapshot
Conformal Regression for Quantitative Structure–Activity Relationship Modeling—Quantifying Prediction Uncertainty
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.8b00124openalex-snapshot
Multi-Descriptor Read Across (MuDRA): A Simple and Transparent Approach for Developing Accurate Quantitative Structure–Activity Relationship Models
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00348openalex-snapshot
Dissecting Machine-Learning Prediction of Molecular Activity: Is an Applicability Domain Needed for Quantitative Structure–Activity Relationship Models Based on Deep Neural Networks?
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00450openalex-snapshot
Chemistry-Wide Association Studies (CWAS): A Novel Framework for Identifying and Interpreting Structure–Activity Relationships
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00499openalex-snapshot
A Distance-Based Boolean Applicability Domain for Classification of High Throughput Screening Data
inferred← citeshttps://doi.org/10.1021/acs.jcim.8b00597openalex-snapshot
Molecular Similarity-Based Domain Applicability Metric Efficiently Identifies Out-of-Domain Compounds
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00135openalex-snapshot
Time-Domain Analysis of Molecular Dynamics Trajectories Using Deep Neural Networks: Application to Activity Ranking of Tankyrase Inhibitors
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00264openalex-snapshot
Building of Robust and Interpretable QSAR Classification Models by Means of the Rivality Index
inferred← citeshttps://doi.org/10.1021/acs.jcim.9b00526openalex-snapshot
Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.1c00020openalex-snapshot
Epigenetic Target Fishing with Accurate Machine Learning Models
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.1c01683openalex-snapshot
Active Learning for Drug Design: A Case Study on the Plasma Exposure of Orally Administered Drugs
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.2c00098openalex-snapshot
Development of Predictive Classification Models for Whole Cell Antimycobacterial Activity of Benzothiazinones
inferred← citeshttps://doi.org/10.1021/acs.jmedchem.5b00865openalex-snapshot
Design, Virtual Screening, and Synthesis of Antagonists of αIIbβ3 as Antiplatelet Agents
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.7b00317openalex-snapshot
ADMET Evaluation in Drug Discovery. Part 17: Development of Quantitative and Qualitative Prediction Models for Chemical-Induced Respiratory Toxicity
inferred← citeshttps://doi.org/10.1021/acs.molpharmaceut.8b01297openalex-snapshot
Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets
inferred← citeshttps://doi.org/10.1021/acsomega.5c05605openalex-snapshot
New Machine Learning Models for Predicting the Organic Cation Transporters OCT1, OCT2, and OCT3 Uptake
inferred← citeshttps://doi.org/10.1021/acsomega.7b00274openalex-snapshot
Experimental Errors in QSAR Modeling Sets: What We Can Do and What We Cannot Do
inferred← citeshttps://doi.org/10.1021/acssuschemeng.7b03795openalex-snapshot
Toward the Rational Design of Sustainable Hair Dyes Using Cheminformatics Approaches: Step 1. Database Development and Analysis
inferred← citeshttps://doi.org/10.1021/acssuschemeng.8b05275openalex-snapshot
Computational Framework for the Identification of Bioprivileged Molecules
inferred← citeshttps://doi.org/10.1021/bk-2016-1222.ch011openalex-snapshot
Generative Topographic Mapping Approach to Chemical Space Analysis
inferred← citeshttps://doi.org/10.1021/ci200091hopenalex-snapshot
A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition
inferred← citeshttps://doi.org/10.1021/ci200409xopenalex-snapshot
Machine Learning Methods for Property Prediction in Chemoinformatics:Quo Vadis?
inferred← citeshttps://doi.org/10.1021/ci200622dopenalex-snapshot
In Silico Assessment of Chemical Biodegradability
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/ci300245qopenalex-snapshot
ToxAlerts: A Web Server of Structural Alerts for Toxic Chemicals and Compounds with Potential Adverse Reactions
inferred← citeshttps://doi.org/10.1021/ci400084kopenalex-snapshot
Time-Split Cross-Validation as a Method for Estimating the Goodness of Prospective Prediction.
inferred← citeshttps://doi.org/10.1021/ci400213dopenalex-snapshot
Development of Dimethyl Sulfoxide Solubility Models Using 163 000 Molecules: Using a Domain Applicability Metric to Select More Reliable Predictions
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/ci500342qopenalex-snapshot
Generalized Workflow for Generating Highly Predictive in Silico Off-Target Activity Models
inferred← citeshttps://doi.org/10.1021/ci500364eopenalex-snapshot
Applicability Domain Based on Ensemble Learning in Classification and Regression Analyses
inferred← citeshttps://doi.org/10.1021/ci5005288openalex-snapshot
How Accurately Can We Predict the Melting Points of Drug-like Compounds?
inferred← citeshttps://doi.org/10.1021/ja206427uopenalex-snapshot
Explanation for Main Features of Structure–Genotoxicity Relationships of Aromatic Amines by Theoretical Studies of Their Activation Pathways in CYP1A2
inferred← citeshttps://doi.org/10.1038/s41598-017-06692-0openalex-snapshot
ADME-Space: a new tool for medicinal chemists to explore ADME properties
inferred← citeshttps://doi.org/10.1038/s41598-019-38508-8openalex-snapshot
Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling
inferred← citeshttps://doi.org/10.1038/s41598-022-19205-5openalex-snapshot
A universal similarity based approach for predictive uncertainty quantification in materials science
inferred← citeshttps://doi.org/10.1039/c9sc00616hopenalex-snapshot
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning
inferred← citeshttps://doi.org/10.1039/d2dd00143hopenalex-snapshot
A SAR and QSAR study on cyclin dependent kinase 4 inhibitors using machine learning methods
inferred← citeshttps://doi.org/10.1039/d3nj05274eopenalex-snapshot
Structure induced activity enhancement of tungsten oxide for tetrabromobisphenol A photodegradation under visible light illumination
inferred← citeshttps://doi.org/10.1039/d3ra08346bopenalex-snapshot
Theoretical insight of reactive oxygen species scavenging mechanism in lignin waste depolymerization products
inferred← citeshttps://doi.org/10.1039/d5cp03617hopenalex-snapshot
Grammatical evolution-based design of nucleotic analogs for SARS-CoV-2's replication-transcription complex
inferred← citeshttps://doi.org/10.1039/d5dd00037hopenalex-snapshot
Machine learning-driven antiviral libraries targeting respiratory viruses
inferred← citeshttps://doi.org/10.1080/07391102.2023.2212062openalex-snapshot
Multitargeted molecular docking and dynamics simulation studies of flavonoids and volatile components from the peel of Citrus sinensis L. (Osbeck) against specific tumor protein markers
inferred← citeshttps://doi.org/10.1080/10590501.2013.763576openalex-snapshot
Comparison of In Silico Models for Prediction of Mutagenicity
inferred← citeshttps://doi.org/10.1080/10590501.2014.877648openalex-snapshot
Performance of (Q)SAR Models for Predicting Ames Mutagenicity of Aryl Azo and Benzidine Based Compounds
inferred← citeshttps://doi.org/10.1080/10590501.2014.938955openalex-snapshot
Evaluation of QSAR Models for the Prediction of Ames Genotoxicity: A Retrospective Exercise on the Chemical Substances Registered Under the EU REACH Regulation
inferred← citeshttps://doi.org/10.1080/10590501.2015.1096881openalex-snapshot
Hierarchical Rules for Read-Across and In Silico Models of Mutagenicity
inferred← citeshttps://doi.org/10.1080/1062936x.2012.762426openalex-snapshot
Comparative analysis of local and consensus quantitative structure-activity relationship approaches for the prediction of bioconcentration factor
inferred← citeshttps://doi.org/10.1080/1062936x.2016.1250229openalex-snapshot
Applicability domain: towards a more formal definition
inferred← citeshttps://doi.org/10.1080/1062936x.2018.1529702openalex-snapshot
Finding the structural requirements of diverse HIV-1 protease inhibitors using multiple QSAR modelling for lead identification
inferred← citeshttps://doi.org/10.1080/1062936x.2019.1644666openalex-snapshot
Rivality index neighbourhood algorithm with density and distances weighted schemes for the building of robust QSAR classification models with high reliable applicability domain
inferred← citeshttps://doi.org/10.1080/1062936x.2020.1725116openalex-snapshot
Quantitative weight of evidence method for combining predictions of quantitative structure-activity relationship models
inferred← citeshttps://doi.org/10.1080/1062936x.2020.1862297openalex-snapshot
SAR and QSAR research on tyrosinase inhibitors using machine learning methods
inferred← citeshttps://doi.org/10.1080/1062936x.2021.1932583openalex-snapshot
QSAR analysis of the acute toxicity of avermectins towards Tetrahymena pyriformis
inferred← citeshttps://doi.org/10.1080/1062936x.2022.2057588openalex-snapshot
Machine learning-based predictive models for identifying high active compounds against HIV-1 integrase
inferred← citeshttps://doi.org/10.1080/1062936x.2024.2375513openalex-snapshot
A SAR and QSAR study on 3CLpro inhibitors of SARS-CoV-2 using machine learning methods
inferred← citeshttps://doi.org/10.1080/19443994.2014.943469openalex-snapshot
Model for predicting transmembrane pressure jump for various membrane bioreactors
inferred← citeshttps://doi.org/10.1093/bib/bbaa374openalex-snapshot
QSAR-assisted-MMPA to expand chemical transformation space for lead optimization
inferred← citeshttps://doi.org/10.1093/intbio/zyaf004openalex-snapshot
Identification of CXCR4 inhibitory activity in natural compounds using cheminformatics-guided machine learning algorithms
inferred← citeshttps://doi.org/10.1093/toxsci/kft209openalex-snapshot
Chemoinformatics Profiling of Ionic Liquids—Automatic and Chemically Interpretable Cytotoxicity Profiling, Virtual Screening, and Cytotoxicophore Identification
inferred← citeshttps://doi.org/10.1101/2024.05.26.595948openalex-snapshot
WITHDRAWN: Physiological modeling into the metaverse of Mycobacterium Tuberculosis Beta CA inhibition mechanism
inferred← citeshttps://doi.org/10.1101/2024.07.02.601740openalex-snapshot
Step Forward Cross Validation for Bioactivity Prediction: Out of Distribution Validation in Drug Discovery
inferred← citeshttps://doi.org/10.1111/cbdd.13657openalex-snapshot
Fingerprint‐based computational models of 5‐lipo‐oxygenase activating protein inhibitors: Activity prediction and structure clustering
inferred← citeshttps://doi.org/10.1124/dmd.120.000320openalex-snapshot
Development of Robust Quantitative Structure-Activity Relationship Models for CYP2C9, CYP2D6, and CYP3A4 Catalysis and Inhibition
inferred← citeshttps://doi.org/10.1177/026119291304100106openalex-snapshot
From Descriptors to Predicted Properties: Experimental Design by Using Applicability Domain Estimation
inferred← citeshttps://doi.org/10.1177/026119291304100107openalex-snapshot
Evaluation of CADASTER QSAR Models for the Aquatic Toxicity of (Benzo)triazoles and Prioritisation by Consensus Prediction
inferred← citeshttps://doi.org/10.1177/026119291404200104openalex-snapshot
The QSPR-THESAURUS: The Online Platform of the CADASTER Project
inferred← citeshttps://doi.org/10.1177/07482337241289184openalex-snapshot
In silico occupational exposure banding framework for data poor compounds in biotechnology
inferred← citeshttps://doi.org/10.1186/1752-153x-5-58openalex-snapshot
The acceptance of in silicomodels for REACH: Requirements, barriers, and perspectives
inferred← citeshttps://doi.org/10.1186/1758-2946-3-51openalex-snapshot
An investigation into pharmaceutically relevant mutagenicity data and the influence on Ames predictive potential
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