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Foundations of molecular modeling and simulation : select papers from FOMMS 2018 / / editors, Edward J. Maginn, Jeffrey Errington
| Foundations of molecular modeling and simulation : select papers from FOMMS 2018 / / editors, Edward J. Maginn, Jeffrey Errington |
| Pubbl/distr/stampa | Singapore : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (x, 220 pages) : illustrations |
| Disciplina | 541.220113 |
| Collana | Molecular modeling and simulation |
| Soggetto topico |
Molecular structure - Computer simulation
Molecules - Models - Computer simulation Quantum chemistry - Data processing |
| ISBN | 981-336-639-7 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- About the Editors -- Strain Controlling Catalytic Efficiency of Water Oxidation for Ni1−xFexOOH Alloy -- 1 Introduction -- 2 Methods and Calculation Details -- 3 Results -- 4 Conclusions -- Appendix -- References -- The Role of Entropy in the Structural Transitions in Zeolitic Imidazolate Frameworks -- 1 Introduction -- 2 Structural Transitions in Zeolitic Imidazolate Frameworks -- 3 Conclusions -- References -- Coarse-Grained Modeling and Simulations of Thermoresponsive Biopolymers and Polymer Nanocomposites with Specific and Directional Interactions -- 1 Introduction -- 2 Oligomers of Nucleic Acids -- 2.1 Background -- 2.2 Model -- 2.3 Method: Simulations and Analyses -- 2.4 Key Results -- 2.5 Limitations and Potential Future Directions -- 3 Collagen-like Peptides -- 3.1 Background -- 3.2 Model -- 3.3 Method: Simulation and Analyses -- 3.4 Key Results -- 3.5 Future Directions -- 4 Polymer Nanocomposites (PNCs) -- 4.1 Background -- 4.2 Model -- 4.3 Method: Simulation and Analyses -- 4.4 Key Results -- 4.5 Limitations and Future Directions -- 5 Conclusions -- References -- Dissipative Particle Dynamics Approaches to Modeling the Self-Assembly and Morphology of Neutral and Ionic Block Copolymers in Solution -- 1 Introduction -- 2 Theory: Micellization of Block Copolymers -- 2.1 Neutral BCP-Based Micelles -- 2.2 Ionic BCP-Based Micelles -- 3 DPD Simulations -- 3.1 Simulations of Neutral Block Copolymers -- 3.2 Ionic Block Copolymers -- 3.3 Charge Calculated pseudo-Explicitly -- 3.4 Charge Incorporated Within Conservative Force -- 4 Summary -- References -- The Statistical Mechanics of Solution-Phase Nucleation: CaCO3 Revisited -- 1 Introduction -- 2 Methods -- 2.1 Construction of a Molecular-Based Potential Model -- 2.2 Determining Cluster Size Distributions -- 3 Results and Discussion.
3.1 The Initial Stages of Nucleation of CaCO3 -- 3.2 Construction of the Two-Component Solution Model -- 3.3 Formalism Between Dynamical and Classical Nucleation Theories -- 4 Summary and Future Overview -- 5 Appendix -- References -- Efficient Sampling of High-Dimensional Free Energy Landscapes: A Review of Parallel Bias Metadynamics -- 1 Introduction -- 2 Theory -- 3 Conclusions -- References -- Coarse-Grained Force Fields Built on Atomistic Force Fields -- 1 Introduction -- 2 Methodologies -- 2.1 The Mapping Rules -- 2.2 Functional Forms -- 2.3 Parameterization -- 3 Representative Cases -- 3.1 Benzene -- 3.2 Small Molecules, Alkanes and Polyethylene -- 3.3 Mixtures of Small Molecules -- 3.4 Water -- 3.5 Electrolyte Aqueous Solutions -- 3.6 PDMS and PEO Polymers -- 4 Conclusions -- Appendix -- On the Temperature Dependency of CGFF -- The Combination Rules -- Simulation Methods -- TEAM-CG Bonded Parameters -- TEAM-CG Non-bonded Parameters -- References -- How Molecular Modelling Tools Can Help in Mitigating Climate Change -- 1 Introduction -- 2 Physicochemical Properties Calculations from Molecular-Based Theories and Molecular Simulations -- 2.1 The Statistical Associating Fluid Theory (SAFT) Equation of State -- 2.2 Molecular Simulations -- 3 Molecular Modelling of New Refrigerants -- 3.1 Pure Component Results -- 3.2 Blends-Results -- 4 CO2 Capture and Separation -- 4.1 Alternative Solvents for CO2 Capture -- 4.2 Novel Adsorbents for CO2 Capture -- 5 Summary and Conclusions -- References. |
| Altri titoli varianti | FOMMS 2018 |
| Record Nr. | UNINA-9910768172003321 |
| Singapore : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Molecular modeling : basic principles and applications / / Hans-Dieter Höltje and Gerd Folkers
| Molecular modeling : basic principles and applications / / Hans-Dieter Höltje and Gerd Folkers |
| Autore | Höltje Hans-Dieter |
| Pubbl/distr/stampa | Weinheim, Germany ; ; New York, New York : , : John Wiley & Sons, , [1997] |
| Descrizione fisica | 1 online resource (209 p.) |
| Disciplina | 572/.33/0113 |
| Collana | Methods and principles in medicinal chemistry |
| Soggetto topico |
Molecules - Models - Computer simulation
Ligand binding (Biochemistry) - Computer simulation Biomolecules - Structure - Computer simulation Drugs - Design - Computer simulation |
| ISBN |
1-281-75846-9
9786611758462 3-527-61477-X 3-527-61476-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Molecular Modeling; Preface; A Personal Foreword; Contents; 1 Introduction; 1.1 Modern History of Molecular Modeling; 1.2 Do Today's Molecular Modeling Methods Illustrate only the Lukretian World?; 1.3 What are Models Used for?; 1.4 Molecular Modeling Uses All FourTypes for Model Building; 1.5 The Final Step is Design; 1.6 The Scope of the Book; 2 Small Molecules; 2.1 Generation of 3D Coordinates; 2.1.1 Crystal Data; 2.1.2 Fragment Libraries; 2.1.3 Sketch Approach; 2.2 Computational Tools for Geometry Optimization; 2.2.1 Force Fields; 2.2.2 Geometry Optimization
2.2.3 Energy-Minimizing Procedures2.2.3.1 Steepest Descent Minimizer; 2.2.3.2 Conjugate Gradient Method; 2.2.3.3 Newton-Raphson Minimizer; 2.2.4 Use of Charges, Solvation Effects; 2.2.5 Quantum Mechanical Methods; 2.2.5.1 Ab initio Methods; 2.2.5.2 Semiempirical Molecular Orbital Methods; 2.3 Conformational Analysis; 2.3.1 Conformational Analysis Using Systematic Search Procedures; 2.3.2 Conformational Analysis Using Monte Carlo Methods; 2.3.3 Conformational Analysis Using Molecular Dynamics; 2.4 Determination of Molecular Interaction Potentials 2.4.1 Molecular Electrostatic Potentials (MEPs)2.4.1.1 Methods for Calculating Atomic Point Charges; 2.4.1.2 Methods for Generating MEPs; 2.4.2 Molecular Interaction Fields; 2.4.2.1 Calculation of GRID Fields; 2.4.2.2 How GRID Fields can be Exploited; 2.4.2.3 Use of Chemometrics:The CoMFA Method; 2.4.3 Hydrophobic Interactions; 2.4.3.1 Log P as a Measure of Lipophilicity; 2.4.3.2 The Hydropathic Field; 2.4.3.3 Display of Properties on a Molecular Surface; 2.5 Pharmacophore Identification; 2.5.1 Molecules to be Matched; 2.5.2 Atom-by-Atom Superposition; 2.5.3 Superposition of Molecular Fields 2.6 The Use of Data Bants2.6.1 Conversion of 2D Structural Data into 3D Form; 2.6.2 3D Searching; 3 Example for Small Molecule Modeling: Serotonin Receptor Ligands; 3.1 Definition of the Serotoninergic Pharmacophore; 3.2 The Molecular Interaction Field; 3.3 Construction of a 5-HT 2a Receptor Binding Site Model; 3.4 Calculation of Interaction Energies; 3.5 Validation of the Model; 4 Introduction to Protein Modeling; 4.1 Where and How to get Information on Proteins; 4.2 Terminology and Principles of Protein Structure; 4.2.1 Conformational Properties of Proteins 4.2.2 Types of Secondary Structural Elements4.2.2.1 The α-Helix; 4.2.2.2 The β-Sheet; 4.2.2.3 Turns; 4.2.3 Homologous Proteins; 4.3 Knowledge-Based Protein Modeling; 4.3.1 Procedures for Sequence Alignments; 4.3.2 Determination and Generation of Structurally Conserved Regions (SCRs); 4.3.3 Construction of Structurally Variable Regions (SVRs); 4.3.4 Side Chain Modeling; 4.3.5 Distance Geometry Approach; 4.3.6 Secondary Structure Prediction; 4.3.7 Energy-Based Modeling Methods; 4.4 Optimization Procedures - Model Refinement - Molecular Dynamics; 4.4.1 Force Fields for Protein Modeling 4.4.2 Geometry Optimization |
| Record Nr. | UNISA-996218390603316 |
Höltje Hans-Dieter
|
||
| Weinheim, Germany ; ; New York, New York : , : John Wiley & Sons, , [1997] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Molecular modeling : basic principles and applications / / Hans-Dieter Höltje and Gerd Folkers
| Molecular modeling : basic principles and applications / / Hans-Dieter Höltje and Gerd Folkers |
| Autore | Höltje Hans-Dieter |
| Pubbl/distr/stampa | Weinheim, Germany ; ; New York, New York : , : John Wiley & Sons, , [1997] |
| Descrizione fisica | 1 online resource (209 p.) |
| Disciplina | 572/.33/0113 |
| Collana | Methods and principles in medicinal chemistry |
| Soggetto topico |
Molecules - Models - Computer simulation
Ligand binding (Biochemistry) - Computer simulation Biomolecules - Structure - Computer simulation Drugs - Design - Computer simulation |
| ISBN |
1-281-75846-9
9786611758462 3-527-61477-X 3-527-61476-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Molecular Modeling; Preface; A Personal Foreword; Contents; 1 Introduction; 1.1 Modern History of Molecular Modeling; 1.2 Do Today's Molecular Modeling Methods Illustrate only the Lukretian World?; 1.3 What are Models Used for?; 1.4 Molecular Modeling Uses All FourTypes for Model Building; 1.5 The Final Step is Design; 1.6 The Scope of the Book; 2 Small Molecules; 2.1 Generation of 3D Coordinates; 2.1.1 Crystal Data; 2.1.2 Fragment Libraries; 2.1.3 Sketch Approach; 2.2 Computational Tools for Geometry Optimization; 2.2.1 Force Fields; 2.2.2 Geometry Optimization
2.2.3 Energy-Minimizing Procedures2.2.3.1 Steepest Descent Minimizer; 2.2.3.2 Conjugate Gradient Method; 2.2.3.3 Newton-Raphson Minimizer; 2.2.4 Use of Charges, Solvation Effects; 2.2.5 Quantum Mechanical Methods; 2.2.5.1 Ab initio Methods; 2.2.5.2 Semiempirical Molecular Orbital Methods; 2.3 Conformational Analysis; 2.3.1 Conformational Analysis Using Systematic Search Procedures; 2.3.2 Conformational Analysis Using Monte Carlo Methods; 2.3.3 Conformational Analysis Using Molecular Dynamics; 2.4 Determination of Molecular Interaction Potentials 2.4.1 Molecular Electrostatic Potentials (MEPs)2.4.1.1 Methods for Calculating Atomic Point Charges; 2.4.1.2 Methods for Generating MEPs; 2.4.2 Molecular Interaction Fields; 2.4.2.1 Calculation of GRID Fields; 2.4.2.2 How GRID Fields can be Exploited; 2.4.2.3 Use of Chemometrics:The CoMFA Method; 2.4.3 Hydrophobic Interactions; 2.4.3.1 Log P as a Measure of Lipophilicity; 2.4.3.2 The Hydropathic Field; 2.4.3.3 Display of Properties on a Molecular Surface; 2.5 Pharmacophore Identification; 2.5.1 Molecules to be Matched; 2.5.2 Atom-by-Atom Superposition; 2.5.3 Superposition of Molecular Fields 2.6 The Use of Data Bants2.6.1 Conversion of 2D Structural Data into 3D Form; 2.6.2 3D Searching; 3 Example for Small Molecule Modeling: Serotonin Receptor Ligands; 3.1 Definition of the Serotoninergic Pharmacophore; 3.2 The Molecular Interaction Field; 3.3 Construction of a 5-HT 2a Receptor Binding Site Model; 3.4 Calculation of Interaction Energies; 3.5 Validation of the Model; 4 Introduction to Protein Modeling; 4.1 Where and How to get Information on Proteins; 4.2 Terminology and Principles of Protein Structure; 4.2.1 Conformational Properties of Proteins 4.2.2 Types of Secondary Structural Elements4.2.2.1 The α-Helix; 4.2.2.2 The β-Sheet; 4.2.2.3 Turns; 4.2.3 Homologous Proteins; 4.3 Knowledge-Based Protein Modeling; 4.3.1 Procedures for Sequence Alignments; 4.3.2 Determination and Generation of Structurally Conserved Regions (SCRs); 4.3.3 Construction of Structurally Variable Regions (SVRs); 4.3.4 Side Chain Modeling; 4.3.5 Distance Geometry Approach; 4.3.6 Secondary Structure Prediction; 4.3.7 Energy-Based Modeling Methods; 4.4 Optimization Procedures - Model Refinement - Molecular Dynamics; 4.4.1 Force Fields for Protein Modeling 4.4.2 Geometry Optimization |
| Record Nr. | UNINA-9910829906903321 |
|
Höltje Hans-Dieter
|
||
| Weinheim, Germany ; ; New York, New York : , : John Wiley & Sons, , [1997] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Molecular modelling : principles and applications / Andrew R. Leach
| Molecular modelling : principles and applications / Andrew R. Leach |
| Autore | Leach, Andrew R. |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Harlow, England ; New York : Prentice Hall, 2001 |
| Descrizione fisica | xxiv, 744 p., [16] p. of plates : ill. (some col.) ; 24 cm |
| Disciplina | 541.2/2/0113 |
| Soggetto topico |
Molecular structure - Computer simulation
Molecules - Models - Computer simulation |
| ISBN | 0582382106 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISALENTO-991004028459707536 |
|
Leach, Andrew R.
|
||
| Harlow, England ; New York : Prentice Hall, 2001 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Statistical modelling of molecular descriptors in QSAR/QSPR [[electronic resource] /] / edited by Matthias Dehmer, Kurt Varmuza, and Danail Bonchev
| Statistical modelling of molecular descriptors in QSAR/QSPR [[electronic resource] /] / edited by Matthias Dehmer, Kurt Varmuza, and Danail Bonchev |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH |
| Descrizione fisica | 1 online resource (458 p.) |
| Disciplina | 572.80285 |
| Altri autori (Persone) |
DehmerMatthias <1968->
VarmuzaKurt <1942-> BonchevDanail |
| Collana | Quantitative and network biology |
| Soggetto topico |
Bioinformatics
Molecules - Models - Computer simulation |
| ISBN |
3-527-64501-2
1-283-59696-2 9786613909411 3-527-64502-0 3-527-64512-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistical Modelling of Molecular Descriptors in QSAR/QSPR; Contents; Preface; List of Contributors; 1 Current Modeling Methods Used in QSAR/QSPR; 1.1 Introduction; 1.2 Modeling Methods; 1.2.1 Methods for Regression Problems; 1.2.1.1 Multiple Linear Regression; 1.2.1.2 Partial Least Squares; 1.2.1.3 Feedforward Backpropagation Neural Network; 1.2.1.4 General Regression Neural Network; 1.2.1.5 Gaussian Processes; 1.2.2 Methods for Classification Problems; 1.2.2.1 Logistic Regression; 1.2.2.2 Linear Discriminant Analysis; 1.2.2.3 Decision Tree and Random Forest; 1.2.2.4 k-Nearest Neighbor
1.2.2.5 Probabilistic Neural Network1.2.2.6 Support Vector Machine; 1.3 Software for QSAR Development; 1.3.1 Structure Drawing or File Conversion; 1.3.2 3D Structure Generation; 1.3.3 Descriptor Calculation; 1.3.4 Modeling; 1.3.5 General purpose; 1.4 Conclusion; References; 2 Developing Best Practices for Descriptor-Based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations; 2.1 Introduction; 2.1.1 Posing the Question; 2.1.2 Validating the Models; 2.1.3 Interpreting the Models; 2.2 Leveraging Experimental Data and Understanding their Limitations 2.3 Descriptors: The Lexicon of QSARs2.3.1 Classical QSAR Descriptors and Uses; 2.3.2 Experimentally Derived Descriptors; 2.3.2.1 Biodescriptors; 2.3.2.2 Descriptors from Spectroscopy/Spectrometry and Microscopy; 2.3.3 0D, 1D and 2D Computational Descriptors; 2.3.4 3D Descriptors and Beyond; 2.3.5 Local Molecular Surface Property Descriptors; 2.3.6 Quantum Chemical Descriptors; 2.4 Machine Learning Methods: The Grammar of QSARs; 2.4.1 Principal Component Analysis; 2.4.2 Factor Analysis 2.4.3 Multidimensional Scaling, Stochastic Proximity Embedding, and Other Nonlinear Dimensionality Reduction Methods2.4.4 Clustering; 2.4.5 Partial Least Squares (PLS); 2.4.6 k-Nearest Neighbors (kNN); 2.4.7 Neural Networks; 2.4.8 Ensemble Models; 2.4.9 Decision Trees and Random Forests; 2.4.10 Kernel Methods; 2.4.11 Ranking Methods; 2.5 Defining Modeling Strategies: Putting It All Together; 2.6 Conclusions; References; 3 Mold2 Molecular Descriptors for QSAR; 3.1 Background; 3.1.1 History of QSAR; 3.1.2 Introduction to QSAR; 3.1.3 Molecular Descriptors: Bridge for QSAR 3.1.3.1 Molecular Descriptors3.1.3.2 Role of Molecular Descriptors; 3.1.3.3 Types of Molecular Descriptors; 3.1.3.4 Calculation of Molecular Descriptors (Software Packages); 3.2 Mold2 Molecular Descriptors; 3.2.1 Description of Mold2 Descriptors; 3.2.1.1 Topological Descriptors; 3.2.1.2 Constitutional Descriptors; 3.2.1.3 Information Content-based Descriptors; 3.2.2 Calculation of Mold2 Descriptors; 3.2.3 Evaluation of Mold2 Descriptors; 3.2.3.1 Information Content by Shannon Entropy Analysis; 3.2.3.2 Correlations between Descriptors; 3.3 QSAR Using Mold2 Descriptors 3.3.1 Classification Models based on Mold2 Descriptors |
| Record Nr. | UNINA-9910130957503321 |
| Weinheim, : Wiley-VCH | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Statistical modelling of molecular descriptors in QSAR/QSPR / / edited by Matthias Dehmer, Kurt Varmuza, and Danail Bonchev
| Statistical modelling of molecular descriptors in QSAR/QSPR / / edited by Matthias Dehmer, Kurt Varmuza, and Danail Bonchev |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Weinheim, : Wiley-VCH |
| Descrizione fisica | 1 online resource (458 p.) |
| Disciplina | 572.80285 |
| Altri autori (Persone) |
DehmerMatthias <1968->
VarmuzaKurt <1942-> BonchevDanail |
| Collana | Quantitative and network biology |
| Soggetto topico |
Bioinformatics
Molecules - Models - Computer simulation |
| ISBN |
3-527-64501-2
1-283-59696-2 9786613909411 3-527-64502-0 3-527-64512-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Statistical Modelling of Molecular Descriptors in QSAR/QSPR; Contents; Preface; List of Contributors; 1 Current Modeling Methods Used in QSAR/QSPR; 1.1 Introduction; 1.2 Modeling Methods; 1.2.1 Methods for Regression Problems; 1.2.1.1 Multiple Linear Regression; 1.2.1.2 Partial Least Squares; 1.2.1.3 Feedforward Backpropagation Neural Network; 1.2.1.4 General Regression Neural Network; 1.2.1.5 Gaussian Processes; 1.2.2 Methods for Classification Problems; 1.2.2.1 Logistic Regression; 1.2.2.2 Linear Discriminant Analysis; 1.2.2.3 Decision Tree and Random Forest; 1.2.2.4 k-Nearest Neighbor
1.2.2.5 Probabilistic Neural Network1.2.2.6 Support Vector Machine; 1.3 Software for QSAR Development; 1.3.1 Structure Drawing or File Conversion; 1.3.2 3D Structure Generation; 1.3.3 Descriptor Calculation; 1.3.4 Modeling; 1.3.5 General purpose; 1.4 Conclusion; References; 2 Developing Best Practices for Descriptor-Based Property Prediction: Appropriate Matching of Datasets, Descriptors, Methods, and Expectations; 2.1 Introduction; 2.1.1 Posing the Question; 2.1.2 Validating the Models; 2.1.3 Interpreting the Models; 2.2 Leveraging Experimental Data and Understanding their Limitations 2.3 Descriptors: The Lexicon of QSARs2.3.1 Classical QSAR Descriptors and Uses; 2.3.2 Experimentally Derived Descriptors; 2.3.2.1 Biodescriptors; 2.3.2.2 Descriptors from Spectroscopy/Spectrometry and Microscopy; 2.3.3 0D, 1D and 2D Computational Descriptors; 2.3.4 3D Descriptors and Beyond; 2.3.5 Local Molecular Surface Property Descriptors; 2.3.6 Quantum Chemical Descriptors; 2.4 Machine Learning Methods: The Grammar of QSARs; 2.4.1 Principal Component Analysis; 2.4.2 Factor Analysis 2.4.3 Multidimensional Scaling, Stochastic Proximity Embedding, and Other Nonlinear Dimensionality Reduction Methods2.4.4 Clustering; 2.4.5 Partial Least Squares (PLS); 2.4.6 k-Nearest Neighbors (kNN); 2.4.7 Neural Networks; 2.4.8 Ensemble Models; 2.4.9 Decision Trees and Random Forests; 2.4.10 Kernel Methods; 2.4.11 Ranking Methods; 2.5 Defining Modeling Strategies: Putting It All Together; 2.6 Conclusions; References; 3 Mold2 Molecular Descriptors for QSAR; 3.1 Background; 3.1.1 History of QSAR; 3.1.2 Introduction to QSAR; 3.1.3 Molecular Descriptors: Bridge for QSAR 3.1.3.1 Molecular Descriptors3.1.3.2 Role of Molecular Descriptors; 3.1.3.3 Types of Molecular Descriptors; 3.1.3.4 Calculation of Molecular Descriptors (Software Packages); 3.2 Mold2 Molecular Descriptors; 3.2.1 Description of Mold2 Descriptors; 3.2.1.1 Topological Descriptors; 3.2.1.2 Constitutional Descriptors; 3.2.1.3 Information Content-based Descriptors; 3.2.2 Calculation of Mold2 Descriptors; 3.2.3 Evaluation of Mold2 Descriptors; 3.2.3.1 Information Content by Shannon Entropy Analysis; 3.2.3.2 Correlations between Descriptors; 3.3 QSAR Using Mold2 Descriptors 3.3.1 Classification Models based on Mold2 Descriptors |
| Record Nr. | UNINA-9910811555203321 |
| Weinheim, : Wiley-VCH | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
