Chemometrics approach for the prediction of chemical compounds' toxicity degree based on quantum inspired optimization with applications in drug discovery
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- @Article{DARWISH:2019:CILS,
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author = "Saad M. Darwish and Tamer A. Shendi and Ahmed Younes",
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title = "Chemometrics approach for the prediction of chemical
compounds' toxicity degree based on quantum inspired
optimization with applications in drug discovery",
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journal = "Chemometrics and Intelligent Laboratory Systems",
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volume = "193",
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pages = "103826",
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year = "2019",
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ISSN = "0169-7439",
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DOI = "doi:10.1016/j.chemolab.2019.103826",
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URL = "http://www.sciencedirect.com/science/article/pii/S0169743918305495",
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keywords = "genetic algorithms, genetic programming, Quantum
computing, Chemometrics, Prediction model",
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abstract = "Chemometrics, the application of mathematical and
statistical methods to the analysis of chemical data,
is finding ever widening applications in the chemical
process environment. The reliable prediction of toxic
effects of chemicals in living systems is highly
desirable in domains such as cosmetics, drug discovery,
food safety, and the manufacturing of chemical
compounds. Toxicity prediction requires several new
approaches for knowledge discovery from data to
paradigm composite associations between the modules of
the chemical compound; the computational demands of
such techniques increase greatly with the number of
chemical compounds involved. State-of-the-art
prediction methods such as neural networks and
multi-layer regression require either tuning parameters
or complex transformations of predictor or outcome
variables and do not achieve highly accurate results.
This paper proposes a Quantum Inspired Genetic
Programming {"}QIGP{"} model to improve prediction
accuracy. Genetic Programming is used to give a linear
equation for calculating the degree of toxicity more
accurately. Quantum computing is employed to improve
the selection of the best-of-run individuals and
handles parsimony pressure to reduce the complexity of
solutions. The results of the internal validation
analysis indicated that the QIGP model has better
goodness of fit statistics then, and significantly
outperforms, the Neural Network model",
- }
Genetic Programming entries for
Saad Mohamed Darwish
Tamer A Shendi
Ahmed Younes
Citations