Masoud Ghods

Accurate prediction of the physicochemical properties of drug compounds is critical for the development of effective and safe antibiotics. In this study, we employ advanced machine learning techniques to address this challenge, using input data that includes M-Polynomials and various physicochemical descriptors. Three models were implemented: basic Support Vector Regression (SVR-Basic), optimized SVR (SVR-Tuned), and Random Forest (RF), trained on known compounds and tested on previously unseen drug samples to evaluate generalization. Model performance was comprehensively assessed using R ² , MSE, RMSE, and MAE, alongside detailed error and residual analyses to ensure precision and robustness. Furthermore, residual-based metrics such as the Mean Residual (MR), Standard Deviation of Residuals (Std Residual), and Interquartile Range (IQR) of Residuals were employed to provide complementary insights into prediction bias, consistency, and robustness. By integrating feature importance analysis and ablation studies, the contribution of each molecular descriptor was systematically evaluated, providing deep insights into model stability and the key factors affecting predictive accuracy. Visual comparisons further illustrated the models’ behavior on training and test datasets. The results demonstrate that the proposed approach not only improves predictive accuracy compared to prior studies but also offers a robust and reliable framework for real-world drug development. All models were implemented in Python 3.12.7, highlighting the practical applicability of machine learning in pharmaceutical research.

This study presents a novel approach to analyzing the physicochemical properties of five antiepileptic drugs through the integration of advanced multi-criteria decision-making techniques, specifically COPRAS and PROMETHEE. The innovation of this research lies in the combination of topological indices with multi-criteria decision-making methods for the ranking of drugs. By employing topological indices, we provide a comprehensive framework for ranking these drugs based on their chemical characteristics without reliance on laboratory experiments. Chemical compounds are typically represented as graphs, where atoms are depicted as vertices and the edges represent the bonds between them. Based on the correlation coefficients between topological indices and the physicochemical characteristics of the drugs, the Forgotten index is identified as a nonbeneficial measure that favors smaller values. In contrast, the Harmonic index, Second Modified Zagreb index, Randić index, and Sum-Connectivity index are considered beneficial, preferring larger values. Furthermore, we utilized Shannon’s entropy method to assign weights to the different criteria, resulting in compelling rankings of the drugs across both decision-making methods. In the COPRAS method, the drugs were ranked as follows: Carbamazepine in first place, followed by Phenytoin, Clonazepam, Clobazam, and Topamax, which were ranked second to fifth, respectively. Conversely, the PROMETHEE method yielded a different ranking order, placing Clonazepam at the top, followed by Clobazam, Carbamazepine, Phenytoin, and Topamax in subsequent positions.

In the article, we review and critique the Corollary and Theorem of “Wiener index of a fuzzy graph and application to illegal immigration networks”, and in addition to providing examples of violations, we modified the Theorem and presented the correct proof using the method of its authors. Keywords: Fuzzy graph; Connectivity index; Wiener index; Fuzzy tree; Fuzzy cycle

Topological indices are numerical values related to a chemical structure that describes the correlation of chemical structure with different physical properties and chemical reactions. Glass has wide applications in architecture, tableware, optics, and optoelectronics. In this article, first, the mathematical relationship between M-polynomial and Banhatti indices such as K-Banhatti, d-Banhatti, and hyper d-Banhatti indices are obtained. Then using M-polynomial, Banhatti indices are calculated.

Graph polynomials based on distance and degree are helpful and necessary because they contain much information about topological indices. In this article, the general inverse sum indeg index and the generalized inverse sum indeg polynomials are calculated for the molecular graph and the line graph of the Titanium dioxide nanotubes. Then, with their help, some polynomials and some degree-based topological indices of the molecular graph and the line graph of Titanium dioxide nanotubes are obtained.

In this study, various beta‐blocker drugs used for heart disease were analyzed, and their degree‐based topological indices derived from the M‐polynomial were calculated. Linear and quadratic regression analysis was used to obtain quantitative structure‐property relationship models between the topological indices and eight the physicochemical properties of the drugs to determine their effectiveness. The results show that the harmonic index was the best predictor for boiling point, flashpoint, and enthalpy of vaporization, while the redefined third Zagreb index was effective for polarizability, molar refractivity, and molar volume. The inverse sum indeg index was found to be effective for molar refractivity, and the second modified Zagreb index was surface tension in linear regression models. In addition, the redefined third Zagreb index was the best predictor for polarizability and molar refractivity, while the second modified Zagreb index was effective for molar volume. The SDD index was found to be effective for surface tension in quadratic regression models.