Dear Editor
The manuscript introduced a novel prediction (measurement) technique based on quantum genetic programming with application on chemical compound toxicity evaluation. The algorithm performance was evaluated on a simulator with real dataset. The reported results are original and of great interest for cheminformatics field, especially in the domain of regression based toxicity estimation.
So, the main contribution of this work is that, several results concerning modeling complex relationships between the structures of the chemical compound using quantum genetic programming is presented to predict and assess the toxicity in chemical compound. In this approach, 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. Therefore, Quantum Inspired Genetic Programming “QIGP” model is able to better give an accurate linear regression between chemical items.
To the best of our knowledge there is no work that utilizes QIGP to build an accurate regression model used for prediction in cheminformatics. There exist many works that utilize neural network, multi-layer regression and traditional genetic programming that either requires tuning parameters or complex transformations of predictor or outcome variables are not achieving high accuracy results. The concept of adapting quantum computing to enhance the operations inside genetic programming; especially in the case of population diversity is used for the first time to take the different relationship between compound’s items into the consideration (items’ implicit relationship). The superiority of the proposed system relies on the fact that QGP a much wider solution space can be analyzed because the structure of the models is not prescribed in advance but is left to the evolutionary process with different probabilities coming from Qubit superposition using quantum rotation gate. By combining the GP and superposition concept, we successfully enhanced the toxicity prediction accuracy, and the result shows that the calculation efficiency of QGP is obviously better than that of classical genetic programming and traditional neural network.
The manuscript is original and unpublished and is not being considered for publication elsewhere. We guarantee that all previously published work cited has been fully acknowledged. To our knowledge there are no issues that would lead to a conflict of interest or disclosure.
We would like to greatly thank the reviewers that will review the manuscript for their fruitful comments, which will help me a lot to improve the manuscript. As you will see, a particular attention has been paid to improve the theoretical background and validating the proposed approach against various kinds of data. I also greatly improved the English usage.
We hope that this work will meet the journal requirements and will be accepted for publication.
