In the United States, cancer is the second leading cause of death. Worldwide too, cancer is a major health problem. Hence, treatment of cancerous tumors remains a matter of very high concern. Apart from surgical treatment, the most commonly employed treatment is chemotherapy. But, due to long-term side effects such as organ damage and loss of teeth, doctors and patients are interested in treatments with reduced side effects. So far, a reasonably acceptable alternative to chemotherapy has not emerged. Recently, 9-anilinoacridines were evaluated as potential antitumor agents due to their enhanced tendency of DNA binding. For an initial evaluation of the drug performance, the association constant, K, is considered to be the key DNA drug binding property. In our work, to reduce experimental efforts and the associated chemical footprint, we develop a QSPR to model K. In our work, to model K, we utilized descriptors requiring representation of molecular structures in two dimensions or less. To establish a relationship between the descriptors and K, we have developed a correlation based adaptive LASSO algorithm (CorrLASSO). CorrLASSO, like LASSO (least absolute shrinkage and selection operator), incorporates feature selection as part of the learning procedure. Also, it is useful for dealing with high-dimensional data. As an improvement, CorrLASSO evaluates correlation between descriptors/features and the dependent property to generate a model with high performance metrics. In our work, R-2, Q(2) and MSE (mean square error) were utilized as performance metrics. (C) 2018 Elsevier Ltd. All rights reserved.