Quantum‐inspired genetic programming model with application to predict toxicity degree for chemical compounds

Abstract

Cheminformatics plays a vital role in maintaining large amounts of chemical data. The reliable prediction of toxic effects of chemicals in living systems is highly desirable in domains such as cosmetics, drug design, 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 multilayer 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 model to improve prediction accuracy. Genetic programming is utilized 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 quantum‐inspired genetic programming model has better goodness‐of‐fit statistics then and significantly outperforms the neural network model.

Progress and prospects of quantum computing in sustainable development: An analytical review

“…A network with 75 layers to categorize various blood cell types with an accuracy of 0.99 on average is proposed. A genetic programming technique based on quantum computing was proposed by Darwish et al (2019) to increase prediction accuracy in linear equations for a more precise determination of the degree of toxicity. The outcomes showed that the proposed model performs better than the neural network model.…”

QIACO: A Quantum Dynamic Cost Ant System for Query Optimization in Distributed Database

“…The main contribution behind this paper is to present how the algorithm is tailored to the problem, containing crossover and mutation operators furthermore setting the overall framework for the utilization of quantum ideas in varied applications. In addition, QIEA is applied along with genetic programming to improve prediction accuracy of toxicity degree of chemical compounds [30]. In this work, the accuracy of linear equation that used to calculate the degree of toxicity increased as a result of using genetic programming.…”

Dynamic Cost Ant Colony Algorithm to Optimize Query for Distributed Database Based on Quantum-Inspired Approach

“…The main contribution behind this paper is to present how the algorithm is tailored to the problem, containing crossover and mutation operators, furthermore setting the overall framework for the utilization of quantum ideas in varied applications. In addition, QIEA is applied along with genetic programming to improve prediction accuracy of toxicity degree of chemical compounds [29]. In this work, the accuracy of the linear equation that was used to calculate the degree of toxicity increased as a result of using genetic programming.…”