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Why QSAR Fails: An Empirical Evaluation Using Conventional Computational Approach
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/qsar.200610151openalex-snapshot
Principles of QSAR models validation: internal and external
inferredcites →https://doi.org/10.1002/qsar.200630020openalex-snapshot
Assessing Applicability Domains of Toxicological QSARs: Definition, Confidence in Predicted Values, and the Role of Mechanisms of Action
inferredcites →https://doi.org/10.1002/qsar.200860186openalex-snapshot
Global or Local QSAR: Is There a Way Out?
inferredcites →https://doi.org/10.1007/978-1-4757-3264-1openalex-snapshot
The Nature of Statistical Learning Theory
inferredcites →https://doi.org/10.1007/bfb0062108openalex-snapshot
The multivariate calibration problem in chemistry solved by the PLS method
inferredcites →https://doi.org/10.1016/b978-012213810-2/50007-4openalex-snapshot
Application of Genetic Algorithms to the General QSAR Problem and to Guiding Molecular Diversity Experiments
inferredcites →https://doi.org/10.1016/j.chemolab.2007.07.004openalex-snapshot
Exploring the impact of size of training sets for the development of predictive QSAR models
inferredcites →https://doi.org/10.1016/j.drudis.2006.06.013openalex-snapshot
Can we estimate the accuracy of ADME–Tox predictions?
inferredcites →https://doi.org/10.1021/ci000045jopenalex-snapshot
The Centroid Approximation for Mixtures: Calculating Similarity and Deriving Structure−Activity Relationships
inferredcites →https://doi.org/10.1021/ci0203702openalex-snapshot
On the Use of Neural Network Ensembles in QSAR and QSPR
inferredcites →https://doi.org/10.1021/ci049850eopenalex-snapshot
Ensemble Methods for Classification in Cheminformatics
inferredcites →https://doi.org/10.1021/ci060117sopenalex-snapshot
On Outliers and Activity CliffsWhy QSAR Often Disappoints
inferredcites →https://doi.org/10.1021/ci100305gopenalex-snapshot
Consensus Ranking Approach to Understanding the Underlying Mechanism With QSAR
inferredcites →https://doi.org/10.1021/ci600299jopenalex-snapshot
Local and Global Quantitative Structure−Activity Relationship Modeling and Prediction for the Baseline Toxicity
inferredcites →https://doi.org/10.1021/ci600563wopenalex-snapshot
Ensemble Feature Selection: Consistent Descriptor Subsets for Multiple QSAR Models
inferredcites →https://doi.org/10.1021/ci700083nopenalex-snapshot
Identifying P-Glycoprotein Substrates Using a Support Vector Machine Optimized by a Particle Swarm
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/ci700404copenalex-snapshot
Combinatorial QSAR Modeling of Specificity and Subtype Selectivity of Ligands Binding to Serotonin Receptors 5HT1E and 5HT1F
inferredcites →https://doi.org/10.1021/ci700443vopenalex-snapshot
Combinatorial QSAR Modeling of Chemical Toxicants Tested against Tetrahymena pyriformis
inferredcites →https://doi.org/10.1021/jm970732aopenalex-snapshot
Three-Dimensional Quantitative Similarity−Activity Relationships (3D QSiAR) from SEAL Similarity Matrices
inferredcites →https://doi.org/10.1021/mp700124eopenalex-snapshot
hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors
inferredcites →https://doi.org/10.1021/tx800252eopenalex-snapshot
Promises and Pitfalls of Quantitative Structure−Activity Relationship Approaches for Predicting Metabolism and Toxicity
inferredcites →https://doi.org/10.1023/a:1025358319677openalex-snapshot
In silico ADME/Tox: why models fail
inferredcites →https://doi.org/10.1145/1656274.1656278openalex-snapshot
The WEKA data mining software
inferredcites →https://doi.org/10.1148/radiology.143.1.7063747openalex-snapshot
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
inferredcites →https://doi.org/10.1289/ehp.5758openalex-snapshot
Methods for reliability and uncertainty assessment and for applicability evaluations of classification- and regression-based QSARs.
inferredcites →https://doi.org/10.1289/txg.7125openalex-snapshot
Assessment of Prediction Confidence and Domain Extrapolation of Two Structure–Activity Relationship Models for Predicting Estrogen Receptor Binding Activity
inferredcites →https://doi.org/10.2174/092986709789578213openalex-snapshot
How to Recognize and Workaround Pitfalls in QSAR Studies: A Critical Review
inferredcites →https://doi.org/10.2174/1568026043451113openalex-snapshot
The Impact of Combinatorial Methodologies on Medicinal Chemistry
inferredcites →https://doi.org/10.2174/156802610790232260openalex-snapshot
3D-QSAR in Drug Design - A Review
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Developing 2D-QSAR models for naphthyridine derivatives against HIV-1 integrase activity
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inferred← citeshttps://doi.org/10.1007/s11030-012-9415-9openalex-snapshot
Reliably assessing prediction reliability for high dimensional QSAR data
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Prediction of Drug Distribution in Rat and Humans Using an Artificial Neural Networks Ensemble and a PBPK Model
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QSAR and docking based lead optimization of nitrogen heterocycles for enhanced prostaglandin EP2 receptor agonistic potency
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QSAR modeling for anti-human African trypanosomiasis activity of substituted 2-Phenylimidazopyridines
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Multiple quantitative structure-activity relationships (QSARs) analysis for orally active trypanocidal N-myristoyltransferase inhibitors
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QSAR modeling approaches to identify a novel ACE2 inhibitor that selectively bind with the C and N terminals of the ectodomain
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Mechanistic QSAR modeling derived virtual screening, drug repurposing, ADMET and in - vitro evaluation to identify anticancer lead as lysine-specific demethylase 5a inhibitor
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Additional file 1 of QSAR, pharmacophore modeling and molecular docking studies to identify structural alerts for some nitrogen heterocycles as dual inhibitor of telomerase reverse transcriptase and human telomeric G-quadruplex DNA
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Novel Quadruplex ligands : in silico and in vitro approaches
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