Developing QSPR for Predicting DNA Drug Binding Affinity of 9-Anilinoacridine Derivatives Using Correlation-Based Adaptive LASSO Algorithm

Abstract

Abstract Major advancements in the field of machine learning and readily available inexpensive computational power, QSPRs (quantitative structure property relationships) are increasingly being viewed, by the scientific community, as reliable tools that can provide accurate property prediction. Additionally, QSPRs offer advantages of experimental cost reduction and reduction in chemical footprint associated with experiments. Treatment of cancerous tumors has become a global focus due to the heightened prevalence of such tumors in humans, both young and old. Apart from surgery, the most commonly used treatment is chemotherapy. As there are many long-term side effects of chemotherapy such as organ damage, fatigue, hair loss and tooth loss, researchers are devoting much attention to the search of treatments with fewer side effects. So far, no effective solution has emerged which can be reported as an alternative to chemotherapy. In a recent study, thirty one (31) 9-anilinoacridines were synthesized and evaluated for their antitumor activity. The association constant, K, was utilized as a key determining factor to evaluate the DNA drug binding affinity. 9-anilinoacridines show great promise as antitumor agents. In order to help reduce the experimental effort of K value determination and to assist in the design of 9-anilinoacridines, in this work, we developed a QSPR to predict K. In order to develop the QSPR, all the structures were drawn and optimized using the Avogadro software and converted to mol files. The Dragon 6 software was then used to calculate the values of descriptors using the generated mol files. The descriptors were then used to develop the model using GA (genetic algorithm) and CorrLASSO (correlation-based adaptive least absolute shrinkage and selection operator). The CorrLASSO in combination with GA helped generate a model with superior prediction as compared with the combination of GA and LASSO (least absolute shrinkage and selection operator) and GA-MLR (genetic algorithm-multiple linear regression). In our work, R2, Q2 and MSE (mean squared error) calculations have been performed to assess model performance and data fitness.