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The topological indices provide quantitative structural characteristics of drug molecules that can be utilized to predict or establish correlations with the biological activity, physicochemical properties, and toxicity of the molecules. Such studies play a crucial role in the initial stages of drug development by aiding in the identification and optimization of potential drug candidates and providing cost-effective techniques for experimental studies. Cancer, a multifaceted disease, can originate in any part of the body due to various factors, including genetic mutations, environmental toxins, and lifestyle choices. Blood cancer, encompassing malignancies affecting the blood, bone marrow, and lymphatic systems, is the focus of this research paper. The current study investigates a comprehensive set of drugs employed in blood cancer treatment, including clofarabine, mercaptopurine, olutasidenib, glasdegib, gliteritinib, zanubrutinib, chlorambucil, ibrutinib, bosutinib, hydroxyurea, cyclophosphamide, doxorubicin, daunorubicin, ivosidenib, prednisone, busulfan, omacetaxine mepesuccinate, and asciminib. By conducting a thorough analysis of these drugs, we acquire valuable insights into their molecular properties, which are crucial for predicting their behavior and efficacy in blood cancer treatment. We have devised QSPR models by leveraging the reverse degree and entropy topological indices. Our proposed QSPR models are compared with existing degree-based models, emphasizing the superior effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Extractive distillation is a common method to separate the mixture, which has the low relative volatility. Many solvents were screened to separate the mixture based on the experiment. Because the number of candidate solvents is high and the experiment is time-consuming. Quantitative structure property relationship was introduced to model and evaluate the relative volatility of isopropanol and water mixture. The model was developed based on genetic function approximation and 10 molecular descriptors were involved in using Material Studio software. The result indicated that the built model was sufficient for predicting the suitable solvent for isopropanol and water mixture. [ABSTRACT FROM AUTHOR]

A quantitative structure–mobility relationship (QSMR) is proposed to estimate the electrophoretic mobility of diverse sets of analyses in capillary zone electrophoresis using Abraham solvation parameters of analyses, such as the excess molar refraction, polarizability, hydrogen bond acidity, basicity, and molar volume. QSMR was developed for prediction the electrophoretic mobility of 231 organic acids using the solvation parameters calculated by Abraham. Multiple linear regression (MLR) as a linear model and artificial neural network (ANN) methods were used to evaluate the nonlinear behavior of the involved parameters. The prediction results are obtained by nonlinear model, ANN, seem to be superior over MLR and were in good agreement with experimental data. In the proposed ANN–QSMR model, the overall mean percentage deviation values were 5.6, 5.4, and 5.3% and the coefficients of determinations (R2) were 0.84, 0.84, and 0.84 for training, test, and verification set, respectively. To investigate the robustness of the model, cross-validation methods have been established, i.e., leave-one-out and leave-N-out (N = 5 and 10) and model is showed good predictive ability against data variation in cross-validation process. This model is not only able to accurately predict the migration order of a diverse set of organic acids but also model finds that solvation parameters are responsible in separation mechanism. [ABSTRACT FROM PUBLISHER]

The ACD/ChemSketch, MarvinSketch, and ChemOffice programmes were used to calculate several molecular descriptors of 51 odorant molecules (15 alcohols, 11 aldehydes, 9 ketones and 16 esters). The best descriptors were selected to establish the Quantitative Structure-Property Relationship (QSPR) of the retention/release property of odorant molecules in pectin gels using Principal Components Analysis (PCA), Multiple Linear Regression (MLR), Multiple Non-linear Regression (MNLR) and Artificial Neural Network (ANN) methods We propose a quantitative model based on these analyses. PCA has been used to select descriptors that exhibit high correlation with the retention/release property. The MLR method yielded correlation coefficients of 0.960 and 0.958 for PG-0.4 (pectin concentration: 0.4% w/w) and PG-0.8 (pectin concentration: 0.8% w/w) media, respectively. Internal and external validations were used to determine the statistical quality of the QSPR of the two MLR models. The MNLR method, considering the relevant descriptors obtained from the MLR, yielded correlation coefficients of 0.978 and 0.975 for PG-0.4 and PG-0.8 media, respectively. The applicability domain of MLR models was investigated using simple and leverage approaches to detect outliers and outside compounds. The effects of different descriptors on the retention/release property are described, and these descriptors were used to study and design new compounds with higher and lower values of the property than the existing ones. [ABSTRACT FROM AUTHOR]

A quantitative structure property relationship model was developed to predict gas to 1-octanol solvation enthalpy (ΔHSolv) of 127 different organic compounds using support vector machine (SVM). The variable selection method of genetic algorithm (GA) was employed to select optimal subset of descriptors. The five descriptors selected by GA were used as inputs for construction of the multiple linear regression (MLR), artificial neural network (ANN) and SVM models. The standard errors of for the prediction data set given by MLR, ANN and SVM were 6.438, 4.004 and 2.711 kJ mol−1, respectively, which indicated that the prediction result agrees well with the experimental values of these compounds and also revealed the superiority of SVM over MLR and ANN models. Furthermore, the derived models also provided some insight into what structural features are related to the gas to ΔHSolvof various organic compounds. [ABSTRACT FROM PUBLISHER]