Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices
Created by W.Langdon from
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- @Article{Goel:2015:JCA,
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author = "Purva Goel and Sanket Bapat and Renu Vyas and
Amruta Tambe and Sanjeev S. Tambe",
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title = "Genetic programming based quantitative
structure-retention relationships for the prediction of
Kovats retention indices",
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journal = "Journal of Chromatography A",
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year = "2015",
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volume = "1420",
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pages = "98--109",
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month = "13 " # nov,
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keywords = "genetic algorithms, genetic programming, Gas
chromatography, Kovats retention index, Quantitative
structure-retention relationships, Artificial
intelligence, Molecular descriptors",
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ISSN = "0021-9673",
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URL = "http://www.sciencedirect.com/science/article/pii/S0021967315014193",
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DOI = "doi:10.1016/j.chroma.2015.09.086",
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abstract = "The development of quantitative structure-retention
relationships (QSRR) aims at constructing an
appropriate linear/nonlinear model for the prediction
of the retention behaviour (such as Kovats retention
index) of a solute on a chromatographic column.
Commonly, multi-linear regression and artificial neural
networks are used in the QSRR development in the gas
chromatography (GC). In this study, an artificial
intelligence based data-driven modelling formalism,
namely genetic programming (GP), has been introduced
for the development of quantitative structure based
models predicting Kovats retention indices (KRI). The
novelty of the GP formalism is that given an example
dataset, it searches and optimizes both the form
(structure) and the parameters of an appropriate
linear/nonlinear data-fitting model. Thus, it is not
necessary to pre-specify the form of the data-fitting
model in the GP-based modelling. These models are also
less complex, simple to understand, and easy to deploy.
The effectiveness of GP in constructing QSRRs has been
demonstrated by developing models predicting KRIs of
light hydrocarbons (case study-I) and adamantane
derivatives (case study-II). In each case study, two-,
three- and four-descriptor models have been developed
using the KRI data available in the literature. The
results of these studies clearly indicate that the
GP-based models possess an excellent KRI prediction
accuracy and generalization capability. Specifically,
the best performing four-descriptor models in both the
case studies have yielded high (>0.9) values of the
coefficient of determination (R2) and low values of
root mean squared error (RMSE) and mean absolute
percent error (MAPE) for training, test and validation
set data. The characteristic feature of this study is
that it introduces a practical and an effective
GP-based method for developing QSRRs in gas
chromatography that can be gainfully used for
developing other types of data-driven models in
chromatography science.",
- }
Genetic Programming entries for
Purva Goel
Sanket Bapat
Renu Vyas
Amruta Tambe
Sanjeev S Tambe
Citations