دانلود کتاب Theoretical and Computational Chemistry: Applications in Industry, Pharma, and Materials Science

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Half Title
Also of Interest
Theoretical and Computational Chemistry: Applications in Industry, Pharma, and Materials Science
Copyright
Preface
Contents
List of contributing authors
1. Computational methods for calculation of protein-ligand binding affinities in structure-based drug design
1.1 Introduction
1.1.1 Components of binding free energy
1.2 Computational chemistry methods in binding affinity calculations
1.2.1 Molecular mechanics
1.2.2 Quantum-mechanics methods
1.2.2.1 QM/MM methods
1.2.2.2 Linear-scaling methods
1.2.2.3 Fragmentation methods
1.3 Virtual screening
1.3.1 Classical SFs
1.3.2 QM scoring functions
1.4 Rescoring of docked ligands and lead optimization
1.4.1 Single-structure approaches
1.4.2 QM/MM methods
1.4.3 Linear-scaling methods
1.4.4 Fragmentation methods
1.4.5 Fully quantum mechanical
1.4.6 End-point approaches
1.4.6.1 QM/MM-Poisson-Boltzmann Surface Area methods
1.4.6.2 Linear interaction energy method
1.4.7 FEP approaches
1.5 Conclusions
References
2. Understanding (coupled) large amplitude motions: the interplay of microwave spectroscopy, spectral modeling, and quantum chemistry
2.1 Introduction
2.2 Spectrometer technology
2.2.1 Resonator-based molecular jet FTMW (2 − 40 GHz) spectrometers: high resolution and sensitivity but time consuming for survey spectra
2.2.2 Chirped-pulse molecular jet FTMW spectrometers: how to reduce the time requirements for survey spectra?
2.3 Quantum chemical calculations
2.3.1 Geometry optimizations: how to start?
2.3.2 Method choice: be careful. Discrepancy!
2.3.3 Basis set choice: make a lot of tests…
2.3.4 Estimation of the torsional barriers: still challenging
2.4 A small historical perspective on large amplitude motions
2.4.1 Internal rotation
2.4.1.1 Symmetric internal rotor
2.4.1.2 Asymmetric internal rotor
2.4.1.3 Why is internal rotation important?
2.4.1.4 Understanding two-top molecules toward astrophysical detections
2.4.1.5 Beyond two internal rotors
2.4.2 Inversion tunneling
2.4.3 Interaction of internal rotation(s) with tunneling motion(s): from rotation wagging to hydrogen transfer
2.5 Spectral modeling
2.5.1 Global fits of rotational spectra with LAMs: the way to achieve standard deviations within experimental accuracy
2.5.1.1 The SPFIT/SPCAT (IAMCALC) package
2.5.1.2 The XIAM code
2.5.1.3 The BELGI code
2.5.1.4 The RAM36 code
2.5.1.5 The ERHAM code
2.5.1.6 The PAM-C2v-2tops code
2.5.2 Separate fits of LAM species: quick check of the assignments
2.5.2.1 Test case 1: a very low barrier (10 cm−1) with C1 symmetry
2.5.2.2 Test case 2: a four-top molecule
2.6 Variety of large amplitude motions in molecules and their applications
2.6.1 Challenges in internal rotation problems: some examples
2.6.1.1 Torsional barriers in acetates: low (100–150 cm−1) and predictable
2.6.1.2 Torsional barriers in acetamides: yet unpredictable
2.6.1.3 Essentially free internal rotation of the propynyl methyl group: very low (<10 cm−1), very challenging
2.6.2 Sensing the molecular conformations of natural substances by internal rotors
2.6.2.1 Acetyl methyl torsion in honey bee pheromones: is the barrier height 180 or 240 cm–1?
2.6.2.2 Conformational determination by internal rotor in lavender oil
2.6.3 Coupled internal rotations
2.6.3.1 From no trouble…
2.6.3.2 …to some troubles
2.6.3.3 …and a lot of troubles
2.6.3.4 But also some nice surprise: lowering the torsional barrier by sterical hindrance
2.6.4 Inversion tunneling
2.7 Conclusions
References
Iwona Gulaczyk and Marek Kręglewski
3. Floppy molecules—their internal dynamics, spectroscopy and applications
3.1 Introduction
3.2 Large amplitude vibrations (LAVs)
3.2.1 Theories involving LAVs
3.2.2 Types of large amplitude vibrations
3.2.2.1 Inversion
3.2.2.2 Internal rotation (torsion)
3.2.2.3 Bending vibration of quasilinear molecules
3.2.2.4 Ring puckering
3.2.2.5 Pseudorotation in a five-membered ring
3.2.2.6 Berry pseudorotation
3.3 Permutation–inversion group theory
3.4 Rovibrational Hamiltonian for a floppy molecule
3.5 Hydrazine molecule
3.5.1 Explicit rovibrational Hamiltonian for hydrazine
3.5.2 Effective rovibrational Hamiltonian for hydrazine
3.6 Floppy molecules applications
References
Donata Pluskota-Karwatka and Marcin Hoffmann
4. Computational studies on statins photoactivity
4.1 Introduction
4.2 Photochemistry of rosuvastatin and pitavastatin
4.3 Photochemistry of fluvastatin
4.4 Photochemistry of atorvastatin
4.5 Effect of pH
4.6 Summary and conclusions
References
5. Artificial intelligence in the modeling of chemical reactions kinetics
5.1 Concise and brief description of the artificial intelligence methods
5.2 Kinetics of chemical reactions in industrial applications
5.3 Reasons for artificial intelligence models use in chemical kinetics
5.4 Selection of recent papers on artificial intelligence methods in prediction of kinetics of various chemical processes
5.4.1 Neural network training with Arrhenius kinetics for equilibrium reactions
5.4.2 Catalytic cracking
5.4.3 Photochemical reactions
5.4.4 Laminar and turbulent combustion modeling
5.4.5 Enzymatic reaction kinetics
5.4.6 Deep learning based on quantum modeling of chemical reactions paths
5.4.7 Coupling microscale kinetics and macroscale chemistry using AI Random Forest algorithms, catalysis on RuO2(110) surface
5.4.8 Hydrogen oxidation
5.5 Summary
References
Magdalena Olkiewicz, Bartosz Tylkowski, Josep M. Montornés, Ricard Garc
6. Modelling of enzyme kinetics: cellulose enzymatic hydrolysis case
6.1 Introduction
6.2 Modelling of enzyme kinetics
6.2.1 The Michaelis-Menten kinetic
6.2.2 Modelling over the years
6.2.3 Classification of models
6.3 Modelling of cellulose enzymatic hydrolysis
6.3.1 Cellulose hydrolysis
6.3.2 Model examples used in cellulose hydrolysis
6.4 Industrial applications
6.5 Conclusions
References
7. Computational approach to the study of morphological properties of morphological properties of polymer/fullerene blends in photovoltaics
7.1 Introduction
7.2 Computational models for polymer blends in BHJ PCs
7.2.1 Course-grained model
7.2.2 The bead-spring model
7.2.3 Process-device model
7.3 Importance of morphological properties in the development of BHJ PCs
7.4 Past, current and future trends in the application of polymer in BHJ PCs
7.4.1 Conjugated polymers used in BHJ PCs
7.4.2 Inverted structure BHJ PCs
7.4.3 Challenges and opportunities
7.5 Conclusion
Notes
References
8. Modeling and assessment of the transfer effectiveness in integrated bioreactor with membrane separation
8.1 Introduction
8.1.1 Scope of application
8.1.2 Research trends
8.2 Research results
8.2.1 Flow behavior
8.2.2 Species transport
8.2.3 Mass transfer
8.3 Discussion
8.3.1 Inference on immersed membrane effectiveness
8.4 Conclusion
References
Index