Beware of q2!
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Summary
It is argued that the high value of LOO q2 appears to be the necessary but not the sufficient condition for the model to have a high predictive power, which is the general property of QSAR models developed using LOO cross-validation.
- Type
- article
- Published
- 2002-01-01
- Cited by
- 3,356
- References
- 39
- OpenAlex
- https://openalex.org/W2087661061
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:7918725
Keywords
Loo, Quantitative structure–activity relationship, Applicability domain, Cross-validation, Test set
References
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- Substituent constants for correlation analysis in chemistry and biology
- An extensive ecdysteroid CoMFA
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- Synthesis, evaluation, and comparative molecular field analysis of 1-phenyl-3-amino-1,2,3,4-tetrahydronaphthalenes as ligands for histamine H(1) receptors.
- A Differential Molecular Connectivity Index
- The electrotopological state: structure information at the atomic level for molecular graphs
- Determination of Topological Equivalence in Molecular Graphs from the Topological State
- E-state fields: Applications to 3D QSAR
- Rational Combinatorial Library Design. 1. Focus-2D: A New Approach to the Design of Targeted Combinatorial Chemical Libraries
- Novel Variable Selection Quantitative Structure-Property Relationship Approach Based on the k-Nearest-Neighbor Principle
- Rational Combinatorial Library Design. 2. Rational Design of Targeted Combinatorial Peptide Libraries Using Chemical Similarity Probe and the Inverse QSAR Approaches
- Novel Indices for the Topological Complexity of Molecules
- Molecular determinants of MAO selectivity in a series of indolylmethylamine derivatives: biological activities, 3D-QSAR/CoMFA analysis, and computational simulation of ligand recognition.
- Substituent constants for correlation analysis.
- Characterization of molecular branching
- Novel Chirality Descriptors Derived from Molecular Topology
- A unified framework for using neural networks to build QSARs.
- Quantitative Structure−Activity Relationship Modeling of Dopamine D1 Antagonists Using Comparative Molecular Field Analysis, Genetic Algorithms−Partial Least-Squares, and K Nearest Neighbor Methods
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- MTD-PLS: A PLS Variant of the Minimal Topologic Difference Method. III. Mapping Interactions between Estradiol Derivatives and the Alpha Estrogenic Receptor
- Supervised Feature Ranking Using a Genetic Algorithm Optimized Artificial Neural Network
- QSAR modeling of GPCR ligands: methodologies and examples of applications.
- In vitro investigations for the QSAR mechanism of lymphocytes apoptosis induced by substituted aromatic toxicants.
- A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest
- Structural investigations of anthranilimide derivatives by CoMFA and CoMSIA 3D-QSAR studies reveal novel insight into their structures toward glycogen phosphorylase inhibition
- Statistical Analysis and Compound Selection of Combinatorial Libraries for Soluble Epoxide Hydrolase
- Mixed learning algorithms and features ensemble in hepatotoxicity prediction
- Docking and 3D‐QSAR investigations of pyrrolidine derivatives as potent neuraminidase inhibitors
- Predictive Modeling of Antioxidant Coumarin Derivatives Using Multiple Approaches: Descriptor-Based QSAR, 3D-Pharmacophore Mapping, and HQSAR
- Structural Determinants of Tau Aggregation Inhibitor Potency*
- Using quantitative structure–activity relationship modeling to quantitatively predict the developmental toxicity of halogenated azole compounds
- Dual inhibitors of Janus kinase 2 and 3 (JAK2/3): designing by pharmacophore- and docking-based virtual screening approach
- QSAR models for HEPT derivates as NNRTI inhibitors based on Monte Carlo method.
- Influence of Iron and Aeration on Staphylococcus aureus Growth, Metabolism, and Transcription
- In vitro Antiviral Effects and 3D QSAR Study of Resveratrol Derivatives as Potent Inhibitors of Influenza H1N1 Neuraminidase
- Validation of chemometric models - a tutorial.
- Quantum Semiempirical Energy Based (SEEB) Descriptors Performance with Benzamidine Inhibitors of Trypsin
- Quantitative Structure‐Activity Relationship Analysis and a Combined Ligand‐Based/Structure‐Based Virtual Screening Study for Glycogen Synthase Kinase‐3
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