The studyofthehealingpowerofchemicalcompounds, present into the known natural active principles and its subsequent use, shall be seen initially as a pseudoscientificprocedure,acontinuationoftheChineseandArabianoccultism, whichwasbased inthepercentagecontentoffire,air,earthandwater,aswellas ontheassociatedqualities:hot-cold,humid-dry...,whichthematterwassupposed to be formed by. Themethod, possessingroots in Hippocrates, Dioscoridesand Galen was studied, described and polished by Avicenna, Averrot!s, Bacon and Villanova inthe MiddleAge. Italsowasappearingasastudyconstantduringthe renaissance and after. On the other hand, the birthofchemistry asascientific offspring from alchemy propitiated alternative ways ofknowledge in order to solve the same problem. In this manner, in the past century, approximately hundred years from now, Sylvester proposed the first molecular description in numerical discrete form, employing ideas which even in present times can be associated withinthesocalledmoleculartopology. Sylvester'stopologicalmodel can be considered the seed allowing the originofthis big tree, which is now knownastheoreticalchemistry. .
During all the past time from the first topological modelofSylvester up to now, the proliferationofnumerical parameters to describe molecular structures has not ceased to grow larger. Some ofthese parameters have played a very important role for the understandingofthe organic molecules behavior and, by extension, for the comprehension and evaluation of their physical as well as biologicalproperties. InthemindofeveryspecialistaretheHammett'scr,theTaft constantsor the octanol-water partition coefficient. Other numerical parameters, suchasthosederivedfromthemodemtopologicalmolecularrepresentationarein aprocessofconstantrevisionandgrowing. Thus,theHosoyaandRandicindices, ortheKier'sconnectivities,amongseveralnotsowellknownnumericaldataare usual reference descriptors. They are putatthe researchers' disposition,andare easily deducible from any molecular representation in form ofordered setsof numerical figures. All ofthem are profusely studied and employed in present times.
The main idea consists into the useofthese numerical data in orderto obtaininformationonthemoleculartrendstopossessoracquirecertainproperties and, even better than this, to determine in which degree or intensity molecules presenteverything.
Reihe
Auflage
Softcover reprint of the original 1st ed. 2000
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für Beruf und Forschung
Research
Illustrationen
Maße
Höhe: 235 mm
Breite: 155 mm
Dicke: 8 mm
Gewicht
ISBN-13
978-3-540-67581-5 (9783540675815)
DOI
10.1007/978-3-642-57273-9
Schweitzer Klassifikation
1 Introduction.- 1.1 Origins and evolution of QSAR.- 1.2 Molecular similarity in QSAR.- 1.3 Scope and contents of the book.- 2 Quantum objects, density functions and quantum similarity measures.- 2.1 Tagged sets and molecular description.- 2.2 Density functions.- 2.3 Quantum objects.- 2.4 Expectation values in Quantum Mechanics.- 2.5 Molecular Quantum Similarity.- 2.6 General definition of molecular quantum similarity measures (MQSM).- 2.7 Quantum self-similarity measures.- 2.8 MQSM as discrete matrix representations of the quantum objects..- 2.9 Molecular quantum similarity indices (MQSI).- 2.10 The Atomic Shell Approximation (ASA).- 2.11 The molecular alignment problem.- 3 Application of Quantum Similarity to QSAR.- 3.1 Theoretical connection between QS and QSAR.- 3.2 Construction of the predictive model.- 3.3 Possible alternatives to the multilinear regression.- 3.4 Parameters to assess the goodness-of-fit.- 3.5 Robustness of the model.- 3.6 Study of chance correlations.- 3.7 Comparison between the QSAR models based on MQSM and other 2D and 3D QSAR methods.- 3.8 Limitations of the models based on MQSM.- 4 Full molecular quantum similarity matrices as QSAR descriptors.- 4.1 Pretreatment for quantum similarity matrices.- 4.2 The MQSM-QSAR protocol.- 4.3 Combination of quantum similarity matrices: the tuned QSAR model.- 4.4 Examples of QSAR analyses from quantum similarity matrices.- 5 Quantum self-similarity measures as QSAR descriptors.- 5.1 Simple QSPR models based on QS-SM.- 5.2 Characterization of classical 2D QSAR descriptors using QS-SM.- 5.3 Description of biological activities using fragment QS-SM.- 6 Electron-electron repulsion energy as a QSAR descriptor.- 6.1 Connection between the electron-electron repulsion energy and QS-SM.- 6.2 ?Vee? as a descriptorfor simple linear QSAR models.- 6.3 Evaluation of molecular properties using ?Vee? as a descriptor.- 7 Quantum similarity extensions to non-molecular systems: Nuclear Quantum Similarity.- 7.1 Generality of Quantum Similarity for quantum systems.- 7.2 Nuclear Quantum Similarity.- 7.3 Structure-property relationships in nuclei.- 7.4 Limitations of the approach.- References.