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Innovative approaches in QSPR modelling using topological indices for the development of cancer treatments
Xiaolong Shi1, Saeed Kosari1, Masoud Ghods2
1Institute of Computing Science and Technology, Guangzhou University, Guangzhou, China.
Abstract:
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.
Insights
Quantitative structure-property relationship (QSPR) models using topological indices (TI) effectively predict physicochemical properties of cancer drugs. These findings highlight TI as valuable tools for developing new cancer therapeutics.
Area of Science:
- Medicinal Chemistry
- Computational Chemistry
- Drug Discovery
Background:
- Cancer remains a critical global health challenge with ongoing research for novel therapeutic agents.
- Topological indices (TI) are increasingly vital in understanding drug properties, aiding molecular, pharmacological, and therapeutic investigations.
- Quantitative Structure-Property Relationships (QSPR) link molecular structure to physicochemical properties, crucial for drug development.
Purpose of the Study:
- To review and analyze the application of topological indices (TI) in Quantitative Structure-Property Relationship (QSPR) modeling for cancer drugs.
- To investigate the predictive power of temperature-based TI for key physicochemical properties of anticancer agents.
- To compare the performance of linear regression, Support Vector Regression (SVR), and Random Forest models in predicting drug properties.
Main Methods:
- A comprehensive review of QSPR studies focusing on topological indices and cancer drugs.
- Application of various topological indices, including temperature-based ones, to predict physicochemical properties.
- Development and validation of regression models (linear regression, SVR, Random Forest) to correlate molecular structure with properties like Boiling Point, Enthalpy, and Molecular Volume.
- Analysis of a diverse set of anticancer drugs, including Aminopterin, Daunorubicin, and Podophyllotoxin.
Main Results:
- Optimal regression models incorporating TI demonstrated significant predictive accuracy for physicochemical properties (BP, EN, FP, MR, PSA, ST, MV, COM).
- Fifty-eight validated QSPR models were identified, showing the robustness of TI in predicting drug characteristics.
- Linear regression models generally performed best, with statistically significant results (p < 0.05).
- SVR and Random Forest models also showed promise, offering complementary insights into QSPR analysis.
Conclusions:
- Temperature-based topological indices are effective and promising tools for predicting physicochemical properties of cancer drugs.
- QSPR modeling using TI provides a valuable framework for the rational design and optimization of novel anticancer agents.
- The study underscores the importance of computational approaches in accelerating cancer drug discovery and development.
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