Novel QSPR modeling of stability constants of metal-thiosemicarbazone complexes by hybrid multivariate technique: GA-MLR, GA-SVR and GA-ANN

Prediction of control temperature and emergency temperature of monadic/binary aromatic nitro compounds by quantitative structure-property relationship: correlation study of self-accelerating decomposition temperature in thermal hazard assessment

CONTEXT

The thermal hazard of reactions caused by the structural instability of aromatic nitro compounds is a major concern in the field of chemical process safety and one of the main causes of major thermal runaway (TR) accidents such as fire and explosion. Among them, the self-accelerating decomposition temperature (SADT), as an important parameter, has been widely used to evaluate the thermal hazards of aromatic nitro compounds in actual storage and transportation processes. However, the control temperature (CT) and emergency temperature (ET), which depend on and are associated with SADT, have been rarely reported in previous studies. In this work, multiple linear regression (MLR) and artificial neural network (ANN) models for CT and ET were constructed based on the molecular descriptors corresponding to the stable structures of 27 monadic/binary aromatic nitro compounds, combined with advanced adiabatic accelerating calorimetric experiments and quantitative structure-property relationship (QSPR). The optimal subset of descriptors with significant contributions was screened out while the fit, predictive ability, and robustness of the four types of models were evaluated with internal and external validation parameters, and finally, two types of parameters (R2 and ARE) were selected as the main indicators for a comprehensive comparative analysis. The results show that the four models fit the experimental data well. During this period, the accuracy of ANN models is slightly higher than that of MLR models, and the QSPR models under the two modes (linear and nonlinear) are more inclined toward ET in prediction ability. Based on simplifying the calculation process and realizing rapid parameter prediction, this study is expected to provide technical support for engineering applications such as safe operation, safe storage and transportation of substances, and emergency response in the chemical industry.

METHODS

In this work, we tested and calculated the thermal safety parameters of 27 monadic/binary aromatic nitro compounds by ARC and AKTS and further used the PubChem database and Gaussian 09 software program to obtain and optimize their corresponding molecular structures. The geometric optimization process adopts DFT on the B3LYP level and the 6-31 + G(d, p) basis set, while the same functional and basis set was used for vibration analysis. The OpenBabel toolbox and ChemDES platform were used for transformation coding and descriptor calculation. Finally, IBM SPSS Statistics 24 and MATLAB software were used to construct MLR models and ANN models, respectively.

Predictive Modeling Analysis for the Quality Indicators of Matsutake Mushrooms in Different Transport Environments

Matsutake mushrooms, known for their high value, present challenges due to their seasonal availability, difficulties in harvesting, and short shelf life, making it crucial to extend their post-harvest preservation period. In this study, we developed three quality predictive models of Matsutake mushrooms using three different methods. The quality changes of Matsutake mushrooms were experimentally analyzed under two cases (case A: Temperature control and sealing measures; case B: Alteration of gas composition) with various parameters including the hardness, color, odor, pH, soluble solids content (SSC), and moisture content (MC) collected as indicators of quality changes throughout the storage period. Prediction models for Matsutake mushroom quality were developed using three different methods based on the collected data: multiple linear regression (MLR), support vector regression (SVR), and an artificial neural network (ANN). The comparative results reveal that the ANN outperforms MLR and SVR as the optimal model for predicting Matsutake mushroom quality indicators. To further enhance the ANN model's performance, optimization techniques such as the Levenberg-Marquardt, Bayesian regularization, and scaled conjugate gradient backpropagation algorithm techniques were employed. The optimized ANN model achieved impressive results, with an R-Square value of 0.988 and an MSE of 0.099 under case A, and an R-Square of 0.981 and an MSE of 0.164 under case B. These findings provide valuable insights for the development of new preservation methods, contributing to the assurance of a high-quality supply of Matsutake mushrooms in the market.

Stability Constant and Potentiometric Sensitivity of Heavy Metal-Organic Fluorescent Compound Complexes: QSPR Models for Prediction and Design of Novel Coumarin-like Ligands

Industrial wastewater often consists of toxic chemicals and pollutants, which are extremely harmful to the environment. Heavy metals are toxic chemicals and considered one of the major hazards to the aquatic ecosystem. Analytical techniques, such as potentiometric methods, are some of the methods to detect heavy metals in wastewaters. In this work, the quantitative structure-property relationship (QSPR) was applied using a range of machine learning techniques to predict the stability constant (logβ_ML) and potentiometric sensitivity (PS_ML) of 200 ligands in complexes with the heavy metal ions Cu²⁺, Cd²⁺, and Pb²⁺. In result, the logβML models developed for four ions showed good performance with square correlation coefficients (R²) ranging from 0.80 to 1.00 for the training and 0.72 to 0.85 for the test sets. Likewise, the PSML displayed acceptable performance with an R² of 0.87 to 1.00 for the training and 0.73 to 0.95 for the test sets. By screening a virtual database of coumarin-like structures, several new ligands bearing the coumarin moiety were identified. Three of them, namely NEW02, NEW03, and NEW07, showed very good sensitivity and stability in the metal complexes. Subsequent quantum-chemical calculations, as well as physicochemical/toxicological profiling were performed to investigate their metal-binding ability and developability of the designed sensors. Finally, synthesis schemes are proposed to obtain these three ligands with major efficiency from simple resources. The three coumarins designed clearly demonstrated capability to be suitable as good florescent chemosensors towards heavy metals. Overall, the computational methods applied in this study showed a very good performance as useful tools for designing novel fluorescent probes and assessing their sensing abilities.

Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer's Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors

Searching for thiosemicarbazone derivatives with the potential to inhibit acetylcholinesterase for the treatment of Alzheimer's disease (AD) is an important current goal. The QSAR_KPLS, QSAR_ANN, and QSAR_SVR models were constructed using binary fingerprints and physicochemical (PC) descriptors of 129 thiosemicarbazone compounds screened from a database of 3791 derivatives. The R ² and Q ² values for the QSAR_KPLS, QSAR_ANN, and QSAR_SVR models are greater than 0.925 and 0.713 using dendritic fingerprint (DF) and PC descriptors, respectively. The in vitro pIC₅₀ activities of four new design-oriented compounds N1, N2, N3, and N4, from the QSAR_KPLS model using DFs, are consistent with the experimental results and those from the QSAR_ANN and QSAR_SVR models. The designed compounds N1, N2, N3, and N4 do not violate Lipinski-5 and Veber rules using the ADME and BoiLED-Egg methods. The binding energy, kcal mol^-1, of the novel compounds to the 1ACJ-PDB protein receptor of the AChE enzyme was also obtained by molecular docking and dynamics simulations consistent with those predicted from the QSAR_ANN and QSAR_SVR models. New compounds N1, N2, N3, and N4 were synthesized, and the experimental in vitro pIC₅₀ activity was determined in agreement with those obtained from in silico models. The newly synthesized thiosemicarbazones N1, N2, N3, and N4 can inhibit 1ACJ-PDB, which is predicted to be able to cross the barrier. The DFT B3LYP/def-SV(P)-ECP quantization calculation method was used to calculate E _HOMO and E _LUMO to account for the activities of compounds N1, N2, N3, and N4. The quantum calculation results explained are consistent with those obtained in in silico models. The successful results here may contribute to the search for new drugs for the treatment of AD.

A novel model for prediction of stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions using structural parameters and multivariate linear regression method

A novel model is presented for reliable estimation of the stability constants of the thiosemicarbazone ligands with different types of toxic heavy metal ions (log β₁₁) in an aqueous solution, which has wide usage in environmental safety and ecotoxicology applications. The biggest reported data of log β₁₁ for 120 metalthiosemicarbazone complexes are used for deriving and testing the novel model. In contrast to available methods where they need the two-dimensional (2D) and three-dimensional (3D) complex molecular descriptors as well as expert users and computer codes, the novel correlation uses four additive and two non-additive structural parameters of thiosemicarbazone ligands. The calculated results of the novel correlation are compared with the outputs of the genetic algorithm with multivariate linear regression method (GA-MLR) as one of the best existing methods, which requires seven complex descriptors. The estimated results for 78 of training as well as 42 of two different test sets were established by external and internal validations. The values of statistical parameters comprising average deviation, average absolute deviation, average absolute relative deviation, absolute maximum deviation, and the coefficient of determination for 73 data of training set of New model/GA-MLR are 0.04/ - 0.25, 1.06/1.31, 14.4/18.7, 3.18/7.92, and 0.830/0.652, respectively. Thus, the predicted results of the new model are worthy as compared to the complex GA-MLR model. Moreover, assessments of various statistical parameters confirm that the new model provides great reliability, goodness-of-fit, accuracy, and precision.

Exploring QSPR models for predicting PUF-air partition coefficients of organic compounds with linear and nonlinear approaches

Partition coefficients are important parameters for measuring the concentration of chemicals by passive sampling devices. Considering the wide application of the polyurethane foam (PUF) in passive air sampling, an attempt for developing several quantitative structure-property relationship (QSPR) models was made in this work, to predict PUF-air partition coefficients (K_PUF-air) using linear (multiple linear regression, MLR) and non-linear (artificial neural network, ANN and support vector machine, SVM) methods by machine learning. All of the developed models were performed on a dataset of 170 compounds comprising 9 distinct classes. A series of statistical parameters and validation results showed that models had good prediction ability, robustness and goodness-of-fit. Furthermore, the underlying mechanisms of molecular descriptors emphasized that ionization potential, molecular bond, hydrophilicity, size of molecule and valence electron number had dominating influence on the adsorption process of chemicals. Overall, the obtained models were all established on the extensive applicability domains, and thus can be used as effective tools to predict the K_PUF-air of new organic compounds or those have not been synthesized yet which, in turn, could help researchers better understand the mechanistic basis of adsorption behavior of PUF.