A new insight on the quantum quantitative structure‐properties relationships

Carbó‐Dorca, Ramon; Damme, Sofie Van

doi:10.1002/qua.21703

Cited by 21 publications

(27 citation statements)

References 57 publications

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“…29, which is also sketched in ref. 35. Indeed, the main KMST structure made of the four steroids, (11), (20), (19), and (10), corresponds to testosteronebinding globulin affinities measuring [27.2], [26.9], [26.9], and [27.4], respectively, constructing thus a certain activity island trend.…”

Section: Kruskal Tree Representationsmentioning

confidence: 99%

“…The provided examples involve several molecular sets, in addition to the socalled Cramer set of steroids, 29,30 which has earlier been subject of several calculations related with QS in our laboratory. [31][32][33][34][35] This application section will be kept to a minimal size, as it has been previously commented that the MQSPS can provide a large amount of information, which can be easily extracted directly from the web site and by using the programs it contains.…”

Section: Introductionmentioning

confidence: 99%

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Communications on quantum similarity, part 3: A geometric‐quantum similarity molecular superposition algorithm

2010

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This work describes a new procedure to obtain optimal molecular superposition based on quantum similarity (QS): the geometric-quantum similarity molecular superposition (GQSMS) algorithm. It has been inspired by the QS Aufbau principle, already described in a previous work, to build up coherently quantum similarity matrices (QSMs). The cornerstone of the present superposition technique relies upon the fact that quantum similarity integrals (QSIs), defined using a GTO basis set, depend on the squared intermolecular atomic distances. The resulting QSM structure, constructed under the GQSMS algorithm, becomes not only optimal in terms of its QSI elements but can also be arranged to produce a positive definite matrix global structure. Kruskal minimum spanning trees are also discussed as a device to order molecular sets described in turn by means of QSM. Besides the main subject of this work, focused on MS and QS, other practical considerations are also included in this study: essentially the use of elementary Jacobi rotations as QSM refinement tools and inward functions as QSM scaling methods.

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Section: Kruskal Tree Representationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Communications on quantum similarity, part 3: A geometric‐quantum similarity molecular superposition algorithm

2010

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“…Therefore, QQSPR, even classical QSPR models, obtained by means of the QQSPR-like algorithms derived in part 1, 16 in previous studies 49,53,54 and also in the initial references, [1][2][3][4] seems that cannot be considered at the same level as the descriptor manipulations leading to empirically defined QSPR models, obtained without any causal background.…”

Section: Atom Number Models and Ockham's Razormentioning

confidence: 99%

Notes on quantitative structure‐properties relationships (QSPR) part 2: The role of the number of atoms as a molecular descriptor

Carbó‐Dorca

Gallegos

2009

J Comput Chem

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A previous analysis performed in our laboratory about the polynomial dependency of the atomic quantum self-similarity measures on the atomic number, together with recent publications on quantitative structure-properties relationships (QSPR), based on the number of molecular atoms, published by various authors, have driven us to show here that a simplified form of the fundamental quantum QSPR (QQSPR) equation, permits to theoretically demonstrate the important, but obvious, role of the number of atoms in a molecule, as a possible molecular descriptor. A discussion of the practical use of the number of atoms in QSPR is also given at the end, which also contains a discussion on the role of Ockham's razor in descriptor simplification choices.

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“…In recent publications, it has been developed the theoretical background, leading into the establishment of a quantum quantitative structure–properties relations (QSPR) equation defined in molecular spaces, resulting from the quantum similarity framework development evolving in time. Recently, a basic concept for this endeavor appears grounded on the description of molecular quantum polyhedra .…”

Section: Introductionmentioning

confidence: 99%

Toward a universal quantum QSPR operator

Carbó‐Dorca

2018

Int J of Quantum Chemistry

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This article presents a discussion about the formalism, which might be associated to a general Quantum quantitative structure–properties relations operator, appearing in a Boltzmann‐like exponential form, which is based in turn on the definition of the concept of thermal voltage, applied to thermally scaled electronic density functions. Three practical numerical examples are presented, corresponding to the calculation of the polarization angle in assorted chiral molecules, the estimation of fish toxicity for perchlorobenzene within the set of chlorobenzenes and a typical quantum QSAR study on the popular Cramer steroid set.

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A new insight on the quantum quantitative structure‐properties relationships

Cited by 21 publications

References 57 publications

Communications on quantum similarity, part 3: A geometric‐quantum similarity molecular superposition algorithm

Communications on quantum similarity, part 3: A geometric‐quantum similarity molecular superposition algorithm

Notes on quantitative structure‐properties relationships (QSPR) part 2: The role of the number of atoms as a molecular descriptor

Toward a universal quantum QSPR operator

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