Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds

International Journal of artificial intelligence research

View Publication Info
 
 
Field Value
 
Title Quantum Inspired Genetic Programming Model to Predict Toxicity Degree for Chemical Compounds
 
Creator Darwish, Saad Mohamed
 
Subject Artificial Intelligence
Cheminformatics; Quantum Computing; Prediction; Genetic Programming
 
Description Cheminformatics plays a vital role to maintain a large amount of chemical data. A reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, food safety, and manufacturing chemical compounds. Toxicity prediction topic requires several new approaches for knowledge discovery from data to paradigm composite associations between the modules of the chemical compound; such techniques need more computational cost as the number of chemical compounds increases. State-of-the-art prediction methods such as neural network and multi-layer regression that requires either tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results.  This paper proposes a Quantum Inspired Genetic Programming “QIGP” model to improve the prediction accuracy. Genetic Programming is utilized to give a linear equation for calculating toxicity degree 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 the solutions. The results of the internal validation analysis indicated that the QIGP model has the better goodness of fit statistics and significantly outperforms the Neural Network model.
 
Publisher STMIK Dharma Wacana
 
Contributor Alexandria University, Egypt
 
Date 2018-06-07
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Identifier http://ijair.id/index.php/ijair/article/view/64
10.29099/ijair.v2i2.64
 
Source International Journal of Artificial Intelligence Research; Vol 3, No 1 (2019): In Pres
2579-7298
10.29099/ijair.v3i1
 
Language en
 
Relation http://ijair.id/index.php/ijair/article/downloadSuppFile/64/16
 
Rights Copyright (c) 2018 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

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