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Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

Journal of chemical information and modeling, 2015-07, Vol.55 (7), p.1316-1322

2015

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Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models

Journal of chemical information and modeling, 2015-07, Vol.55 (7), p.1316-1322

2015

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Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors

Journal of medicinal chemistry, 2018-04, Vol.61 (8), p.3582-3594

2018

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Beware of R^sup 2^: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

Journal of chemical information and modeling, 2015-07, Vol.55 (7), p.1316

2015

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Chemoinformatics approaches to virtual screening

1, 2008

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Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure-Activity Relationship Models of Animal Carcinogenicity

Environmental health perspectives, 2008-04, Vol.116 (4), p.506-513

2008

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Application of Validated QSAR Models of D sub(1) Dopaminergic Antagonists for Database Mining

Journal of medicinal chemistry, 2005-11, Vol.48 (23), p.7322-7332

2005

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Quantitative Structure−Activity Relationship Modeling of Dopamine D1 Antagonists Using Comparative Molecular Field Analysis, Genetic Algorithms−Partial Least-Squares, and K Nearest Neighbor Methods

Journal of medicinal chemistry, 1999-08, Vol.42 (17), p.3217-3226

1999

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4.07 - Predictive Quantitative Structure–Activity Relationship Modeling

Comprehensive Medicinal Chemistry II, 2007, Vol.4, p.149-165

Tropsha, A.

Second Edition, 2007

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Relative Binding Free Energies of Peptide Inhibitors of HIV-1 Protease: The Influence of the Active Site Protonation State

Journal of medicinal chemistry, 1995-01, Vol.38 (1), p.42-48

1995

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Automated Computation of Materials Properties

Materials Informatics, 2019

2019

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Cognitive Chemistry: The Marriage of Machine Learning and Chemistry to Accelerate Materials Discovery

Materials Informatics, 2019

2019

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From Topological Descriptors to Expert Systems: A Route to Predictable Materials

Materials Informatics, 2019

2019

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Crystallography Open Database: History, Development, and Perspectives

Materials Informatics, 2019

2019

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Modeling Materials Quantum Properties with Machine Learning

Materials Informatics, 2019

2019

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Pauling File: Toward a Holistic View

Materials Informatics, 2019

2019

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A High‐Throughput Computational Study Driven by the AiiDA Materials Informatics Framework and the PAULING FILE as Reference Database

Materials Informatics, 2019

2019

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Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation

Materials Informatics, 2019

2019

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The Inorganic Crystal Structure Database (ICSD): A Tool for Materials Sciences

Materials Informatics, 2019

2019

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QSAR analysis of nitroaromatics' toxicity in Tetrahymena pyriformis: structural factors and possible modes of action

SAR and QSAR in environmental research, 2011-01, Vol.22 (5-6), p.575-601

2011

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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 H1 Receptors

Journal of medicinal chemistry, 1999-08, Vol.42 (16), p.3041-3054

1999

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The "random-coil" state of proteins: Comparison of database statistics and molecular simulations

Proteins, structure, function, and bioinformatics, 1999-09, Vol.36 (4), p.407-418

1999

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Preface

Chemoinformatics approaches to virtual screening, 2008

2008

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Chemoinformatics approaches to virtual screening, 2008

2008

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Subject Index

Chemoinformatics approaches to virtual screening, 2008

2008

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Beware of R(2): Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

Beware of R2: simple, unambiguous assessment of the prediction accuracy of QSAR and QSPR models

Computer-Aided Discovery and Characterization of Novel Ebola Virus Inhibitors

Beware of R^sup 2^: Simple, Unambiguous Assessment of the Prediction Accuracy of QSAR and QSPR Models

Chemoinformatics approaches to virtual screening

Use of Cell Viability Assay Data Improves the Prediction Accuracy of Conventional Quantitative Structure-Activity Relationship Models of Animal Carcinogenicity

Application of Validated QSAR Models of D sub(1) Dopaminergic Antagonists for Database Mining

Quantitative Structure−Activity Relationship Modeling of Dopamine D1 Antagonists Using Comparative Molecular Field Analysis, Genetic Algorithms−Partial Least-Squares, and K Nearest Neighbor Methods

4.07 - Predictive Quantitative Structure–Activity Relationship Modeling

Relative Binding Free Energies of Peptide Inhibitors of HIV-1 Protease: The Influence of the Active Site Protonation State

Automated Computation of Materials Properties

Cognitive Chemistry: The Marriage of Machine Learning and Chemistry to Accelerate Materials Discovery

From Topological Descriptors to Expert Systems: A Route to Predictable Materials

Crystallography Open Database: History, Development, and Perspectives

Modeling Materials Quantum Properties with Machine Learning

Pauling File: Toward a Holistic View

A High‐Throughput Computational Study Driven by the AiiDA Materials Informatics Framework and the PAULING FILE as Reference Database

Machine Learning Interatomic Potentials for Global Optimization and Molecular Dynamics Simulation

The Inorganic Crystal Structure Database (ICSD): A Tool for Materials Sciences

QSAR analysis of nitroaromatics' toxicity in Tetrahymena pyriformis: structural factors and possible modes of action

Synthesis, Evaluation, and Comparative Molecular Field Analysis of 1-Phenyl-3-amino-1,2,3,4-tetrahydronaphthalenes as Ligands for Histamine H1 Receptors

The "random-coil" state of proteins: Comparison of database statistics and molecular simulations

Preface

Contents

Subject Index

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