Àâòîðèçàöèÿ
Ïîèñê ïî óêàçàòåëÿì
Leach A.R. — Molecular Modelling Principles and Applications
Îáñóäèòå êíèãó íà íàó÷íîì ôîðóìå
Íàøëè îïå÷àòêó? Âûäåëèòå åå ìûøêîé è íàæìèòå Ctrl+Enter
Íàçâàíèå: Molecular Modelling Principles and Applications
Àâòîð: Leach A.R.
Àííîòàöèÿ: Preface to the Second Edition The impetus for this second edition is a desire to include some of the new techniques that have emerged in recent years and also extend the scope of the book to cover certain areas that were under-represented (even neglected) in the first edition. In this second volume there are three topics that fall into the first category (density functional theory, bioinformatics/protein structure analysis and chemoinformatics) and one main area in the second category (modelling of the solid state). In addition, of course, a new edition provides an opportunity to take a critical view of the text and to re-organise and update the material. Thus whilst much remains from the first edition, and this second book follows much the same path through the subject, readers familiar with the first edition will find some changes which I hope they will agree are for the better. As with the first edition we initially consider quantum mechanics, but this is now split into two chapters. Thus Chapter 2 provides an introduction to the ab initio and semi-empirical approaches together with some examples of the uses of quantum mechanics. Chapter 3 covers more advanced aspects of the ab initio approach, density functional theory and the particular problems of the solid state. Molecular mechanics is the subject of Chapter 4 and then in Chapter 5 we consider energy minimisation and other 'static' techniques. Chapters 6, 7 and 8 deal with the two main simulation methods (molecular dynamics and Monte Carlo). Chapter 9 is devoted to the conformational analysis of 'small' molecules but also includes some topics (e.g. cluster analysis, principal components analysis) that are widely used in informatics. In Chapter 10 the problems of protein structure prediction and protein folding are considered; this chapter also contains an introduction to some of the more widely used methods in bioinformatics. In Chapter 11 we draw upon material from the previous chapters in a discussion of free energy calculations, continuum solvent models, and methods for simulating chemical reactions and defects in solids. Finally, Chapter 12 is concerned with modelling and chemoinformatics techniques for discovering and designing new molecules, including database searching, docking, de novo design, quantitative structure-activity relationships and combinatorial library design. As in the first edition, the inexorable pace of change means that what is currently considered 'cutting edge' will soon become routine. The examples are thus chosen primarily because they illuminate the underlying theory rather than because they are the first application of a particular technique or are the most recent available. In a similar vein, it is impossible in a volume such as this to even attempt to cover everything and so there are undoubtedly areas which are under-represented. This is not intended to be a definitive historical account or a review of the current state-of-the-art. Thus, whilst I have tried to include many literature references it is possible that the invention of some technique may appear to be incorrectly attributed or a 'classic' application may be missing. A general guiding principle has been to focus on those techniques that are in widespread use rather than those which are the province of one particular research group. Despite these caveats I hope that the coverage is sufficient to provide a solid introduction to the main areas and also that those readers who are 'experts' will find something new to interest them.
ßçûê:
Ðóáðèêà: Õèìèÿ /
Ñòàòóñ ïðåäìåòíîãî óêàçàòåëÿ: Ãîòîâ óêàçàòåëü ñ íîìåðàìè ñòðàíèö
ed2k: ed2k stats
Èçäàíèå: 2-nd
Ãîä èçäàíèÿ: 2001
Êîëè÷åñòâî ñòðàíèö: 774
Äîáàâëåíà â êàòàëîã: 21.02.2007
Îïåðàöèè: Ïîëîæèòü íà ïîëêó |
Ñêîïèðîâàòü ññûëêó äëÿ ôîðóìà | Ñêîïèðîâàòü ID
Ïðåäìåòíûé óêàçàòåëü
Locally enhanced sampling 575—576
LogP 668—670
London force 204—205
Long time-tails, molecular dynamics 377
Long-range correction 327
Long-range forces and computer simulation 334—343
Loop conformations 541—542
Lorentz — Berthelot mixing rules 210
Low-mode search 478—479
Lowdin population analysis 80
Lower-energy regions 564
Lowest unoccupied molecular orbit 79 112 293—294
LR (linear response) 588—589 591 631—632
LSDFT (local spin density functional theory) 129 135
LUDI program 689
LUMO (lowest unoccupied molecular orbit) 79 112 293—294
Lysine 510 525 556—557
MACCS system 645
Maclaurin series 11
Magnesium 238 623 626
Many-body, effects in empirical potentials 212—214
Many-body, perturbation theory 114—117
Many-body, potentials 241
Mapping, adiabatic 286
Mapping, distance 651
Mapping, pharmacophore 648
Mapping, Ramachandran 459—460 514 543 547
Marker atom 329—330
Markov chain 414—415
Markov models, hidden 536—537 538
Marsaglia random number generator 420 453—454
Mass-weighted coordinates 274—275
Mathematical concepts 10—24
Mathematical concepts, complex numbers 16—18
Mathematical concepts, multiple integrals 19—20
Mathematical concepts, series expansions 10—11
Mathematical concepts, statistics 20—21 (see also “Eigenvalues” “Fourier” “Lagrange” “Matrices” “Vectors”)
Matrices 2—3 9 12—16 415
Matrices, adjacency 647
Matrices, charge density 58—59
Matrices, distance 652
Matrices, elastic constant 296—297
Matrices, PAM 524—526 531 556—557
Matrices, positive definite 16 258
Matrices, statistical weight 430
Matrices, stochastic 415 (see also “Fock matrix” “Hessian” “Z-matrix”)
Maxima 273
Maximal segment pair 531—533
Maximum dissimilarity algorithms 683—684
Maximum likelihood method 657—658
MaxSum and MaxMin 683—684 685
Maxwell — Boltzmann distribution 365 367 384
Mayer bond order 83
Mc see “Monte Carlo”
MCSCF (multiconfiguration SCF) 113
MCSS (multiple-copy simultaneous search) 688
MDL (Molecular Design mol) format 643—644
Mean field approach 307—309
Mean square end-to-end distance, polymers 426
Mean squared displacement 322—323
Mechanics, molecular see “Force field”
Mesoscale modelling 402—404
Messenger RNA (mRNA) 509
Met-enkephalin 517
Metals 147 589 607 626 649—650 693
Metals, force field potentials for 240—245
Methane, bond order 83
Methane, force fields 189
Methane, Monte Carlo simulation 441—442
Methane, octopole moment 76
Methane, population analysis 79
Methane, SMILES notation 644
Methanol 573
Methionine 511 525 556—555
Methyl chloride 612—63 614 676—667
Methylalanine 583—58
Methylene group 160 162 330 396 448
Methylene group, energy minimisation methods 280 291
Methylene group, force fields 181 221
Metric matrix 469
Metrisation 472
Metropolis Monte Carlo simulation 306 433 436 437 447
Metropolis Monte Carlo simulation, conformational analysis 467 505
Metropolis Monte Carlo simulation, implementation 417—420
Metropolis Monte Carlo simulation, new molecules 663 685 691
Metropolis Monte Carlo simulation, proteins 518
Metropolis Monte Carlo simulation, theoretical background 414—416
Microcanonical ensemble, definition 307
MINDO/3 86 94—96 102—103
Minima 272—273
Minimal basis set 69—70
Minimisation see “Energy minimisation”
Minimum image convention and computer simulation 324—334
Mixing rules 210
MM2/MM3/MM4 programs 8 615
MM2/MM3/MM4 programs, force fields 169—171 173 176 179 187 211 233—234
MNDO (modified neglect of diatomic overlap) 86 96—97 98—99 102—103 192
MOD function 418—419
Modeller program 541 549
Modified INDO (MINDO/3) 86 94—96 102—103
Modified neglect see “MNDO”
Molar refractivity 671
Molecular dynamics simulation 354—409 623
Molecular dynamics simulation of chain amphiphiles 394—404
Molecular dynamics simulation, computer simulation 305—306 307
Molecular dynamics simulation, conformational analysis 457 475—476 483—489
Molecular dynamics simulation, conformational changes from 392—393
Molecular dynamics simulation, constant pressure dynamics 385—387
Molecular dynamics simulation, constant temperature dynamics 382—385
Molecular dynamics simulation, constraint dynamics 368—374
Molecular dynamics simulation, continuous methods 355—364
Molecular dynamics simulation, energy conservation in 405—406
Molecular dynamics simulation, ensemble 653
Molecular dynamics simulation, free energy calculations 564 572 577 579 581 588 616—622 628
Molecular dynamics simulation, Monte Carlo compared with 307 387 452—453
Molecular dynamics simulation, new molecules 664
Molecular dynamics simulation, proteins 552
Molecular dynamics simulation, setting up and running 364—368
Molecular dynamics simulation, simple models 353—354
Molecular dynamics simulation, solvent effects 387—390
Molecular dynamics simulation, time-dependent properties 374—382 (see also “Computer simulation”)
Molecular field analysis 708—711
Molecular fitting 490—491
Molecular fragments see “Fragments”
Molecular modelling see “Advanced ab initio” “Computer concepts conformational “Energy force “Free “Molecular Monte new “Proteins” “Quantum
Molecular orbital theories, semi-empirical 86 89—96 102—103
Molecular surface see “Surface”
Moller — Plesset see “MP”
Moments theorem 241—2
Monomers 289—290 423 550
Monomers, new molecules 712—713 717—718
Monte Carlo simulation 410—456
Monte Carlo simulation, bias 432—433 443—450
Monte Carlo simulation, chemical potential, calculating 442—443
Monte Carlo simulation, computer 306—307
Monte Carlo simulation, conformational analysis 457 475—476 479 483 504—505
Monte Carlo simulation, density functional theory 130
Monte Carlo simulation, different ensembles, sampling from 438—442
Monte Carlo simulation, force fields 189
Monte Carlo simulation, free energy calculations 564 577 579 588
Monte Carlo simulation, free energy calculations, chemical reactions 613 616
Monte Carlo simulation, free energy calculations, PMF 581—582 584
Monte Carlo simulation, free energy calculations, solid-state defects 623 628
Monte Carlo simulation, free energy calculations, thermodynamic perturbation 572—573
Monte Carlo simulation, Gibbs ensemble 450—451
Monte Carlo simulation, integration, calculating properties by 412—414
Monte Carlo simulation, molecular dynamics compared with 307 387 452—453
Monte Carlo simulation, molecules 420—423
Monte Carlo simulation, molecules, new 662—663 685 691
Monte Carlo simulation, polymers 423—431
Monte Carlo simulation, proteins 517—519 551
Monte Carlo simulation, quasi ergodicity 433—438
Monte Carlo simulation, random number generators 418—420 453—454 Metropolis”)
Monte Carlo, configurational bias 443—450
Monte Carlo, force-bias 432
Monte Carlo, Grand canonical 440—442
Monte Carlo, smart 432
MOP AC program 8 99
Morgan algorithm 644
Morokuma analysis 122—124
Morse potential/curve 170—172 210
Motifs 522
Mott — Littleton method 623—624 625—627
MP (Moller — Plesset) perturbation theory 114 115—116 119
MR (molar refractivity) 671
MS (Murtaugh — Sargent) method 269—270
MSP (maximal segment pair) 531—533
Mthoxypromazine 678
Mulliken population analysis 79—80 189
Multicanonical Monte Carlo simulation 435—438
Multiconflguration SCF 113
Multiple integrals 19—20
Multiple linear regression 666 699 702
Multiple sequence alignment 534—537
Multiple-copy simultaneous search 688
Multipole, electric, calculation of 75—77
Multipole, fast 341—343 364
Multipole, models 195—197 219
Multivariate problems 708
Murtaugh — Sargent method 269—270
Mutation, operator 480
Mutation, probability matrices for proteins 556—557
N, N-dimethyl-ketopropanamide 230
naphthalene 233
NCC (Nieser — Corongiu — Clementi) model 219—220
NDDO (neglect of diatomic differential overlap) 86 93—94 95 96
Nearly free-electron approximation 142 147—153
Needleman — Wunsch algorithm 526—529 534
Neglect of differential overlap 86 89—96
Neighbour lists 325—327 493—496
Net (partial) atomic charges 157—159 181
Net dipole moment 378—379
Netropsin 270—271
Neural networks and QASR 703—705
New molecules 640—726
New molecules, 3D 674—675 687
New molecules, 3D, databases 659—661 679
New molecules, 3D, pharmacophores 648—659 674—675 687
New molecules, 3D, searching 645 647 667—668
New molecules, 3D, similarity 678—679 (see also “QSAR”)
New molecules, combinatorial libraries 711—719
New molecules, computer representations 642—647
New molecules, de novo structure based ligand design 687—694
New molecules, descriptors 668—679
New molecules, discovery of drugs 640—641
New molecules, diverse sets of compounds, selecting 680—687
New molecules, docking 661—668 689
New molecules, partial least squares 702 706—711
New molecules, similarity 668 676—679
Newton — Raphson energy minimisation 267—268 270 288 625
Newton’s laws 304 309 353 366 371
Niching 481
Nickel oxide 147
Nicotine/nicotinic pharmacophore 653 678
Nitrogen 490
Nitrogen, amide 660
Nitrogen, basis sets 73
Nitrogen, bond order 83
Nitrogen, charge models 187—8
Nitrogen, distributed multipole model 196
Nitrogen, electrostatic potentials 188
Nitrogen, force fields 181
Nitrogen, substituents 693
NM23 547
NMR and X-ray crystallography 316
NMR and X-ray crystallography, conformational analysis 468 474—475 483—489 490
NMR and X-ray crystallography, molecular dynamics simulation 379 383 395
NMR and X-ray crystallography, new molecules 647 659 661 667 689 691 693 704 713
NMR and X-ray crystallography, proteins 516 522 546—547 552 512 514
Nodes on graphs 642—643
Nodes on search trees 461
NOESY (nuclear Overhauser enhancement spectroscopy) 474—475 486 488
Non-bonded cutoffs 324—334
Non-bonded interactions 166 181—212 324
Non-bonded interactions, cell multipole method for 341—343
Non-bonded interactions, electrostatic 166 181—204
Non-bonded interactions, neighbour lists 325—327
Non-bonded interactions, Van der Waals 166 204—212
Non-derivative energy minimisation 258—261
Non-electrostatic contributions to solvation free energy calculations 608—609
Non-holonormc constraints 370
Non-periodic boundary methods 320—321
Normal distribution see “Gaussian functions”
Normal mode analysis and energy minimisation 273—278
Normal vibrational modes 274
Nuclear Overhauser see “NOESY”
Nucleic acids 196—197
o-methylacetanilide 670
Octopole 76 181
One-electron atoms 30—34
One-electron integrals 50—51
ONIOM approach 615
Onsager dipole model 593—595
Open-shell systems 108—110
Operators 28—29 53 57 114 480—481
OPLS (optimised parameters for liquid simulations) 210 228 599
Orbital approximate theories 86
Orbital calculations, molecular 26 41—51
Orbital electronegativity 192—193
Orbital energy of closed-shell system 51
Orbital energy of general polyelectronic system 46—50
Orbital hydrogen 41—46
Orbital linear combination of atomic 41—42 56 100 241
Orbital one- and two-electron integrals 50—51
Orbital semi-empirical 86 89—96 102—103
Orbital semi-empirical, total electron density 77—79 (see also “STOs”)
Orbital virtual 61 (see also “Kohn — Sham”)
Orbital-based approach to band theory 142—146
Order of integration algorithm 358
Order, bond 81—83
Order, parameters 321—322
Orientational correlation 379—380
Orthogonalisation, symmetric 60
Orthonormal wavefunctions 30
Oscillating charge 201—202
Out-of-plane bending 176—178
Outside-in Hgand design 687—688
Overlap, differential 86 88—96
Overlap, forces see “Repulsive forces”
Overlap, integral 52
Oxides 147 238
Oxygen bonds/interactions 98 237 328 620 652
Pairwise potential models 240—241
PAM matrices 524—526 531 556—557
Parameters 567 599
Parameters, force field 221 224—225 228—232
Parameters, substituent 695—697 (see also “Verlet”)
Partial (net) atomic charges 157—159 181
Partial equalisation of orbital, electronegativity 192—193
Partial least squares (PLS) 702 706—711
Partition, coefficients 572—573 668—671
Partition, electron density 80—81
Partition, free energy 574—576
Partition/partitioning 683—685
Partitioning, coefficients 572—573 668—671
Partitioning, electron density 80—81
Partitioning, free energy 574—576
Pattern recognition 491—497
Pauli principle 206
PCA (principal components analysis) 497—499 681 686
Ðåêëàìà