Modeling and quantitative structure-activity study of some carboxylate derivatives as anticancer drugs using multivariate linear regression and artificial neural networks

Subject Areas :

mehdi  nekoei ¹ (Islamic Azad University of shahrood branch)
Mahmood  Ebrahimi ² (IAU)
پرستو  فتاحی ³ (IAU)
Behzad  Chahkandi ⁴ (IAU)

Received: 2020-10-19 Accepted : 2020-10-26 Published : 2022-05-22

Keywords: carboxylate derivatives, Artificial Neural Network, multiple linear regression, Quantitative structure-activity relationship, Drug resistance,

Abstract :

Chemotherapy is one of the most effective treatments for cancers, but many cancers become resistant to the therapeutic effects of a drug during treatment with chemotherapy, which is called Multi Drug Resistance. Currently, some new drugs, including carboxylate derivatives, have been used to reduce drug resistance. In the present study, a structure-activity quantitative relationship (QSAR) study was performed to predict the drug activity of some carboxylate derivatives using multivariate linear regression (MLR) and artificial neural networks (ANN). First, the structure of drug compounds, drawing and appropriate group of descriptors were calculated. Then, the step selection method was used to obtain the best descriptors that were most related to the drug activity of the compounds. First, the linear model of multiple linear regression (MLR) was developed. ANN was then used to obtain better results. Statistical data show the superiority of ANN method over MLR method.