Modern <scp>2D QSAR</scp> for drug discovery

Lewis, Richard A.; Wood, David

doi:10.1002/wcms.1187

Cited by 58 publications

(28 citation statements)

References 98 publications

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“…Also it must be said that the present author opinion is not prone to acknowledge the fact that many comparable structure–properties relations papers, which have been published in large number in recent and past times, have been overemphasizing their scientific importance, just based on statistical confidence testing grounds, see for example recent studies in Refs . While such statistical attachment of all these studies proves nothing more than a linear relationship exists or not, and in what extent, for instance between two vectors.…”

Section: Computational Applications Over Some Assorted Molecular Setsmentioning

confidence: 81%

Communications on quantum similarity (4): collective distances computed by means of similarity matrices, as generators of intrinsic ordering among quantum multimolecular polyhedra

Carbó‐Dorca

Barragán

2015

WIREs Comput Mol Sci

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This study generalizes the notion of distance via defining an axiomatic collective distance, between arbitrary vector sets. A first part discusses conceptual tools, which can be later useful for general mathematical practice or as computational quantum similarity indices. After preliminary definitions, two elements, which can be associated with arbitrary sets of a vector space, are described: the centroid and the variance vectors. The Minkowski norm of the variance vector is shown to comply with the axioms of a collective distance. The role of the Gram matrix, associated with a vector set, is linked to the definition of numerical variance. Several simple application examples involving linear algebra and N-dimensional geometry are given. In a second part, all previous definitions are applied to quantum multimolecular polyhedra (QMP), where a set of molecular quantum mechanical density functions act as vertices. The numerical Minkowski norm of the variance vector in any QMP could be considered as a superposition of molecular contributions, corresponding to a new set of quantum similarity indices, which can generate intrinsic ordering among QMP vertices. In this way, the role of quantum similarity matrix elements is evidenced. Application to collections of molecular structures is analyzed as an illustrative practical exercise. The connection of the QMP framework with classical and quantum quantitative structure-properties relation (QSPR) becomes evident with the aid of numerical examples computed over several molecular sets acting as QMP.

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Section: Computational Applications Over Some Assorted Molecular Setsmentioning

confidence: 81%

Communications on quantum similarity (4): collective distances computed by means of similarity matrices, as generators of intrinsic ordering among quantum multimolecular polyhedra

Carbó‐Dorca

Barragán

2015

WIREs Comput Mol Sci

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“…Digital systems complement this, through graphical searching of structures and substructures, making tangible the idea of chemical structure as language [87]. The associated concept of structural similarity has been applied with considerable success to systems for information retrieval and for structure-property correlation [88][89][90][91][92][93].…”

Section: Organising Conceptsmentioning

confidence: 99%

Storing the Wisdom: Chemical Concepts and Chemoinformatics

Bawden

2015

Informatics

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The purpose of the paper is to examine the nature of chemical concepts, and the ways in which they are applied in chemoinformatics systems. An account of concepts in philosophy and in the information sciences leads to an analysis of chemical concepts, and their representation. The way in which concepts are applied in systems for information retrieval and for structure-property correlation are reviewed, and some issues noted. Attention is focused on the basic concepts or substance, reaction and property, on the organising concepts of chemical structure, structural similarity, periodicity, and on more specific concepts, including two-and three-dimensional structural patterns, reaction types, and property concepts. It is concluded that chemical concepts, despite (or perhaps because of) their vague and mutable nature, have considerable and continuing value in chemoinformatics, and that an increased formal treatment of concepts may have value in the future.

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“…activity, toxicity, physicochemical properties, etc.). At the same time, QSAR provides one of the most effective strategies for predicting properties of new chemicals and also for identifying potential hits through virtual screening of chemical libraries [1,2]. The last few decades have witnessed several transformations in the field of QSAR modelling, owing to the progress in model development strategies, data mining techniques, validation methodologies, along with machine learning and statistical analysis tools [3].…”

Section: Introductionmentioning

confidence: 99%

QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR Modelling

Cordeiro

Halder

2020

Preprint

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Quantitative structure activity relationships (QSAR) modelling is a well-known computational tool, often used in a wide variety of applications. Yet one of the major drawbacks of conventional QSAR modelling tools is that models are set up based on a limited number of experimental and/or theoretical conditions. To overcome this, the so-called multitasking or multi-target QSAR (mt-QSAR) approaches have emerged as new computational tools able to integrate diverse chemical and biological data into a single model equation, thus extending and improving the reliability of this type of modelling. We have developed QSAR-Co-X, an open source python−based toolkit (available to download at https://github.com/ncordeirfcup/QSAR-Co-X) for supporting mt-QSAR modelling following the Box-Jenkins moving average approach. The new toolkit embodies several functionalities for dataset selection and curation plus computation of descriptors, for setting up linear and non-linear models, as well as for a comprehensive results analysis. The workflow within this toolkit is guided by a cohort of multiple statistical parameters along with graphical outputs onwards assessing both the predictivity and the robustness of the derived mt-QSAR models. To monitor and demonstrate the functionalities of the designed toolkit, three case-studies pertaining to previously reported datasets are examined here. We believe that this new toolkit, along with our previously launched QSAR-Co code, will significantly contribute to make mt-QSAR modelling widely and routinely applicable.

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Modern 2D QSAR for drug discovery

Cited by 58 publications

References 98 publications

Communications on quantum similarity (4): collective distances computed by means of similarity matrices, as generators of intrinsic ordering among quantum multimolecular polyhedra

Communications on quantum similarity (4): collective distances computed by means of similarity matrices, as generators of intrinsic ordering among quantum multimolecular polyhedra

Storing the Wisdom: Chemical Concepts and Chemoinformatics

QSAR-Co-X: An Open Source Toolkit for Multi-Target QSAR Modelling

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