Current Mathematical Methods Used in QSAR/QSPR Studies

Liu, Peixun; Long, Wei

doi:10.3390/ijms10051978

Cited by 182 publications

(91 citation statements)

References 111 publications

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“…New descriptors have been introduced, including topological indices [1], group bond contributions [2], and a wide range of classical or quantum descriptors [3,4]. On the other hand, in addition to linear models, neural networks [5] and related artificial intelligence approaches [6], as well as support vector machines [7] and other learning methods [8] are now in use to predict complex properties.…”

Section: Introductionmentioning

confidence: 99%

Inductive modeling of physico-chemical properties: Flash point of alkanes

Mathieu

2010

Journal of Hazardous Materials

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Section: Introductionmentioning

confidence: 99%

Inductive modeling of physico-chemical properties: Flash point of alkanes

Mathieu

2010

Journal of Hazardous Materials

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“…Recent advances in statistical, algorithmic and chemoinformatics in relation to LBDD have been discussed elsewhere in depth 39-42 . This chapter will briefly overview LBDD followed by a detailed presentation of new developments in the areas of conformational sampling and force fields (FF) with respect to LBDD.…”

Section: Introductionmentioning

confidence: 99%

Computational ligand-based rational design: role of conformational sampling and force fields in model development

Shim¹,

MacKerell²

2011

Med. Chem. Commun.

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A significant number of drug discovery efforts are based on natural products or high throughput screens from which compounds showing potential therapeutic effects are identified without knowledge of the target molecule or its 3D structure. In such cases computational ligand-based drug design (LBDD) can accelerate the drug discovery processes. LBDD is a general approach to elucidate the relationship of a compound's structure and physicochemical attributes to its biological activity. The resulting structure-activity relationship (SAR) may then act as the basis for the prediction of compounds with improved biological attributes. LBDD methods range from pharmacophore models identifying essential features of ligands responsible for their activity, quantitative structure-activity relationships (QSAR) yielding quantitative estimates of activities based on physiochemical properties, and to similarity searching, which explores compounds with similar properties as well as various combinations of the above. A number of recent LBDD approaches involve the use of multiple conformations of the ligands being studied. One of the basic components to generate multiple conformations in LBDD is molecular mechanics (MM), which apply an empirical energy function to relate conformation to energies and forces. The collection of conformations for ligands is then combined with functional data using methods ranging from regression analysis to neural networks, from which the SAR is determined. Accordingly, for effective application of LBDD for SAR determinations it is important that the compounds be accurately modelled such that the appropriate range of conformations accessible to the ligands is identified. Such accurate modelling is largely based on use of the appropriate empirical force field for the molecules being investigated and the approaches used to generate the conformations. The present chapter includes a brief overview of currently used SAR methods in LBDD followed by a more detailed presentation of issues and limitations associated with empirical energy functions and conformational sampling methods.

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“…Classical methods, such as single or multiple linear regression (MLR), partial least squares (PLS), neural networks (NN), and support vector machine (SVM), are being upgraded by improving the kernel algorithms or by combining them with other methods, including novel approaches, such as gene expression programming (GEP), project pursuit regression (PPR), and local lazy regression (LLR) [28].…”

Section: Regression Analysismentioning

confidence: 99%

Computational Modeling of Silicate Glasses: A Quantitative Structure-Property Relationship Perspective

Pedone

Menziani

2015

Molecular Dynamics Simulations of Disordered Materials

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This article reviews the present state of Quantitative Structure-Property\ud Relationships (QSPR) in glass design and gives an outlook into future developments.\ud First an overview is given of the statistical methodology, with particular emphasis\ud to the integration of QSPR with molecular dynamics simulations to derive informative\ud structural descriptors. Then, the potentiality of this approach as a tool for\ud interpretative and predictive purposes is highlighted by a number of recent inspiring\ud applications

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Current Mathematical Methods Used in QSAR/QSPR Studies

Cited by 182 publications

References 111 publications

Inductive modeling of physico-chemical properties: Flash point of alkanes

Inductive modeling of physico-chemical properties: Flash point of alkanes

Computational ligand-based rational design: role of conformational sampling and force fields in model development

Computational Modeling of Silicate Glasses: A Quantitative Structure-Property Relationship Perspective

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