Innovative approaches in QSPR modelling using topological indices for the development of cancer treatments. - Xiaolong Shi; Saeed Kosari; Masoud Ghods; Negar Kheirkhahan (PLoS ONE)
This paper provides a comprehensive review of quantitative structure-property relationships (QSPR) about to cancer drugs, with a focus on the application of topological indices (TI) and data analysis techniques. Cancer is a serious and life-threatening disease for which no complete cure currently exists. Consequently, extensive research is ongoing to develop new therapeutic agents. The application of topological indices in chemistry and medicine, particularly in the investigation of the molecular, pharmacological, and therapeutic properties of drugs, has become a significant tool. This article investigates the potential of Temperature indices in analyzing the physicochemical properties of drugs used for cancer treatment. The approach employs QSPR modeling to establish correlations between the molecular structure of a compound and its physical and chemical properties. The analysis covers a range of Cancer drugs, including Aminopterin, Convolutamide A, Convolutamydine A, Daunorubicin, Minocycline, Podophyllotoxin, Caulibugulone E, Perfragilin A, Melatonin, Tambjamine K, Amathaspiramide E, and Aspidostomide E. The findings demonstrate that optimal regression models (Fifty-eight models) incorporating TI can effectively predict physicochemical properties, such as Boiling Point (BP), Enthalpy (EN), Flash Point (FP), Molar Refractivity (MR), Polar Surface Area (PSA), Surface Tension (ST), Molecular Volume (MV), and Complexity (COM). This research suggests that temperature-based topological indices (TI) are promising tools for the development and optimization of cancer drugs, as demonstrated by statistically significant results with a p-value less than 0.05. In addition to the linear regression model, which performed the best, two other machine learning models, namely SVR and Random Forest, were also used for further analysis and comparison of their performance in predicting the physicochemical properties of drugs, to assess the advantages and disadvantages of each model.