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Three Useful Dimensions for Domain Applicability in QSAR Models Using Random Forest

https://doi.org/10.1021/ci300004n
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Outgoing claims: the papers this work cites, builds on, or argues with.

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The One‐Class Classification Approach to Data Description and to Models Applicability Domain
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Target‐Driven Subspace Mapping Methods and Their Applicability Domain Estimation
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Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules
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The National Center for Biotechnology Information
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Can we estimate the accuracy of ADME–Tox predictions?
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Assessing the reliability of a QSAR model's predictions
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The importance of the domain of applicability in QSAR modeling
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Fingerprint-based clustering applied to define a QSAR model use radius
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Atom pairs as molecular features in structure-activity studies: definition and applications
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Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling
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Blood-Brain Barrier Permeation Models: Discriminating between Potential CNS and Non-CNS Drugs Including P-Glycoprotein Substrates
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/ci0500379openalex-snapshot
Boosting: An Ensemble Learning Tool for Compound Classification and QSAR Modeling
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/ci1000967openalex-snapshot
Assessment of Methods To Define the Applicability Domain of Structural Alert Models
inferredcites →https://doi.org/10.1021/ci100253ropenalex-snapshot
Applicability Domains for Classification Problems: Benchmarking of Distance to Models for Ames Mutagenicity Set
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Predictive Models for Cytochrome P450 Isozymes Based on Quantitative High Throughput Screening Data
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Contemporary QSAR Classifiers Compared
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Structure−Activity Landscape Index: Identifying and Quantifying Activity Cliffs
inferredcites →https://doi.org/10.1021/ci800151mopenalex-snapshot
Critical Assessment of QSAR Models of Environmental Toxicity against Tetrahymena pyriformis: Focusing on Applicability Domain and Overfitting by Variable Selection
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/ci900161gopenalex-snapshot
Benchmark Data Set for in Silico Prediction of Ames Mutagenicity
inferredcites →https://doi.org/10.1021/ci900313uopenalex-snapshot
Chemical Domain of QSAR Models from Atom-Centered Fragments
inferredcites →https://doi.org/10.1021/ci950274jopenalex-snapshot
Chemical Similarity Using Physiochemical Property Descriptors

What points at this work

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

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Additional file 1 of MAIP: a web service for predicting blood‐stage malaria inhibitors
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Additional file 1 of A hybrid framework for improving uncertainty quantification in deep learning-based QSAR regression modeling
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Estimation de l'écotoxicité de substances chimiques par des méthodes à noyaux
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