Statistical Modelling of Molecular Descriptors in QSAR/QSPR 2012
DOI: 10.1002/9783527645121.ch2
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Abstract: For well over 100 years, chemists have explored the relationship between the chemical structure and biological activity, and dreamed of predicting them as well as other measurable properties. The first description of a relationship between composition and activity [1] was based on observations of correlation between specific molecular features and observable physiochemical properties [2]. With some data tabulation, it was found that structure-activity relationships could be used to quantify chemical intuition:… Show more

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Cited by 9 publications
(7 citation statements)
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“…216 Similar lists were proposed also elsewhere. [217][218][219] One of these criteria is always that a topological index "should change gradually with gradual change in (molecular) structure". This property may be called the smoothness of the topological index in question.…”
Section: Advanced Comparative Testing Of Degree-based Topological Indmentioning
confidence: 99%
“…216 Similar lists were proposed also elsewhere. [217][218][219] One of these criteria is always that a topological index "should change gradually with gradual change in (molecular) structure". This property may be called the smoothness of the topological index in question.…”
Section: Advanced Comparative Testing Of Degree-based Topological Indmentioning
confidence: 99%
“…However, as in reality the model’s landscape is not entirely smooth, it is crucial to map rugged regions across chemical space, since identifying these regions is the only way of assuring that the model is being safely used for future predictions [41.] The applicability domain establishes where the QSAR is smooth (i.e., where the dependency between structure and property holds).…”
Section: Resultsmentioning
confidence: 99%
“…Carefully curated literature data for the polar and dispersive components of surface energy of 30 polymers [1222] and 33 functionalized silica surfaces, [2325] all obtained by the contact angle method and geometric mean formulation [26] were used to train and validate the surface energy MQSPR models using Multiple Linear Regression (MLR), Partial Least-Squares (PLS), or Support Vector Machine (SVM) regression, based on “best practices” methods in machine learning and model validation. [3] The model with the best performance was then selected for use in the modeling workflow system. The descriptors used in the polymer MQSPR models are based on electron density distribution topology as well as the distribution of Electrostatic Potential (EP) and Active Lone Pair (ALP) potential on the molecular van der Waals surfaces evaluated using MOE (Molecular Operating Environment) software (Figure 2a).…”
Section: Resultsmentioning
confidence: 99%
“…[2] Heuristic techniques have been shown, previously, to be very powerful in chemistry and biology for predicting, for example, the behavior of drug-like molecules as potential therapeutic agents. [3,4] …”
Section: Introductionmentioning
confidence: 99%