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

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.