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Ebola and Marburg viruses, biosafety level 4 pathogens, cause severe hemorrhaging and organ failure with high mortality. Although some FDA-approved vaccines or therapeutics like Ervebo for Zaire Ebola virus exist, still there is a lack of effective therapeutics that cover all filoviruses, including both Ebola and Marburg viruses. Therefore, some anti-filovirus drugs such as Pinocembrin, Favipiravir, Remdesivir and others are used to manage infections. In theoretical chemistry, a chemical molecule is converted into an isomorphic molecular graph, G (V , E) by considering atom set V as vertices and bond set E as edges. A topological index is a molecular descriptor derived from the molecular graph of a chemical compound that characterizes its topology. The relationship between a compound's chemical structure and its properties is investigated through the quantitative structure-property relationship (QSPR). This article introduces new reverse sum Revan degree based indices to explore the physicochemical and pharmacokinetic properties of anti-filovirus drugs via multilinear regression. The findings reveal a strong correlation between these proposed indices and the properties of anti-filovirus drugs when compared to reverse and Revan degree-based indices. Thus, reverse sum Revan indices offer valuable insights for analyzing the drugs properties used to treat Ebola and Marburg virus infections. Moreover, the multilinear regression (MLR) results through reverse sum Revan indices are compared with Artificial Neural Network (ANN) modelling technique and it provides the better prediction of the physicochemical and pharmacokinetic properties of anti-filovirus drugs. [ABSTRACT FROM AUTHOR]

Topological indices are the real numbers related to structure of graphs. We consider the Sombor topological index for p-type graphs and carry its deep analysis, characterize graphs with extremum values and order them on the basis of the Sombor index and Sombor energy. Also, we give QSPR analysis of some allergy drugs with the various topological indices, study their correlation coefficient, coefficient of determination, linear, polynomial and logarithmic analysis. A significant observation is that a better relation exists between physical properties of drugs with these indices which in turn improves their efficiency and advances their design structure. [ABSTRACT FROM AUTHOR]

The present article aims, as far as is known for the first time, to investigate and indeed to causally justify the cycle of a molecular machine, in the present case for rotaxanes formed by the combination of axons and wheel, based on the wave functions associated with various stages in the evolution of molecules involved in the cycle of molecular machines. The purpose of the article is to investigate the relationship between the wave functions Ψ1, Ψ2, and Ψ3, associated with the no-complex, pre-complex, and complex stages, respectively, for a molecular machine type evolution composed of the molecular structure DB24C8 and the set of axons numbered 1+–10+, with ionic charge. [ABSTRACT FROM AUTHOR]

Benzene separation from hydrocarbon mixtures is a challenge in the refining and petrochemical industries. The application of liquid–liquid extraction process using ionic liquids (I.Ls) is an option for this separation. The selection of the most appropriate I.L. for this application is a challenging task due to the variety of anion and cation structures. In the current study, the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been evaluated using the Quantitative Structure–Property Relationship (QSPR) method. A dataset comprising of 112 ternary systems (namely, I.L., benzene, and aliphatic hydrocarbon) was compiled after an extensive review of literature. The primary dataset consists of 17 anions, 20 cations, and 12 aliphatic hydrocarbons. Therefore, the impact of the structure of anion, cation, or aliphatic hydrocarbon on the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases has been investigated. The linear QSPR models were constructed using Multiple Linear Regression (MLR). The statistical evaluation of the final linear model showed that the constructed model (R2 = 0.900) has an acceptable capability to predict the mole fraction of benzene in the I.L.-rich phase. Additionally, non-linear QSPR models were developed using Genetic Programming (GP) and Artificial Neural Network (ANN) machine learning methods. The statistical evaluation of the GP model (R2 = 0.927) and ANN model (R2 = 0.939) showed that non-linear models had slightly higher prediction accuracy compared to the linear model. The final QSPR model was developed using the BELe3 cation descriptor which is a 2D Burden eigenvalues descriptor and HTm anion descriptor which is a 3D GETAWAY descriptor. After model construction, the selected molecular descriptors of anion and cation structures has been interpreted. The results showed that the size and the electronegativity of the atoms in the anion and cation structure are probably important parameters that affect the benzene distribution between the aliphatic hydrocarbon-rich and I.L.-rich phases. Additionally, the anion shape can be considered as an effective parameter in the benzene extraction process. [ABSTRACT FROM AUTHOR]

Transpiration method is used to determine the vapor pressures of diesters of malic acid and linear alcohols (C1–C5) over the 303–369 K range of temperatures. The enthalpies of vaporization of the esters at 298.2 K are calculated from the resulting data. Correlations between the enthalpies of vaporization, the Kovats indices, and the number of carbon atoms are derived. The contributions from hydroxyl groups and intermolecular hydrogen bonding to the overall enthalpy are determined. A modified version of the established QSPR procedure is developed for calculating the enthalpies of vaporization of hydroxy acid esters. [ABSTRACT FROM AUTHOR]

To predict the biological effects of chemical compounds based on mathematical and statistical relationships, quantitative structure–activity relationship (QSAR) approach is used. Based on the molecular characteristics of diverse substances, Quantitative Structure–Property Relationship (QSPR) techniques estimate the physiochemical attributes whereas Quantitative Structure Toxicity Relationship (QSTR) is used as a link between the molecular structure of species and its toxicity. These ligand-based computational screening methods offer a cost-effective replacement for laboratory-based screening procedures. Different QSTR models are established to understand the biological activities related to toxicity. Density Functional Theory (DFT) and ab-initio techniques are used to examine external acute toxicity using Quantum Chemical (QC) descriptors and the electron correlation contribution. Conceptual Density Functional Theory (CDFT) based global and local descriptors have wide applications in analysing various physical and chemical characteristics of chemical species. The descriptors like chemical hardness, electronegativity, electrophilicity index, HOMO–LUMO energy, and enthalpy are found reliable to predict the model in terms of available experimental data. Various mathematical models are established through Multi Linear Regression (MLR) analysis which links the calculated descriptors with their biological activities. In this review, the applications of CDFT-based descriptors, are described in detail for QSAR / QSPR/ QSTR studies. [ABSTRACT FROM AUTHOR]

The melting points of ionic liquids (ILs) reported since 2020 were surveyed, collected, and reviewed, which were further combined with the previous data to provide a database with 3129 ILs ranging from 177.15 to 645.9 K in melting points. In addition, the factors that affect the melting point of ILs from macro, micro, and thermodynamic perspectives were summarized and analyzed. Then the development of the quantitative structure-property relationship (QSPR), group contribution method (GCM), and conductor-like screening model for realistic solvents (COSMO-RS) for predicting the melting points of ILs were reviewed and further analyzed. Combined with the evaluation together with the preliminary study conducted in this work, it shows that COSMO-RS is more promising and possible to further improve its performance, and a framework was thus proposed. [ABSTRACT FROM AUTHOR]

This work demonstrates that the transport properties of various organic substances in the gas phase, such as viscosity and thermal conductivity coefficients, can be estimated with acceptable accuracy by using 1D-QSPR models, which allow for the prediction of the studied property based solely on the chemical composition of the molecule. Here, we studied the transport properties (viscosity coefficient and thermal conductivity coefficient) of organic compounds for sufficiently representative database including approximately 5,000 carbon-, halogen-, oxygen-, nitrogen-, and sulfur-containing compounds. Using a simplex approach for modeling molecular structure and machine learning methods, such as multiple linear regression (MLR) and random forest (RF), adequate 1D QSPR models for the transport properties of individual substances in the gas phase were developed for the formed databases. Analysis of the influence of certain structural and physicochemical factors on the studied transport properties of organic compounds was carried out. Based on the developed 1D RF QSPR models, a computer expert system for predicting the viscosity coefficients and thermal conductivity of new substances was created. [ABSTRACT FROM AUTHOR]

A key step in building regulatory acceptance of alternative or non-animal test methods has long been the use of interlaboratory comparisons or round-robins (RRs), in which a common test material and standard operating procedure is provided to all participants, who measure the specific endpoint and return their data for statistical comparison to demonstrate the reproducibility of the method. While there is currently no standard approach for the comparison of modelling approaches, consensus modelling is emerging as a “modelling equivalent” of a RR. We demonstrate here a novel approach to evaluate the performance of different models for the same endpoint (nanomaterials’ zeta potential) trained using a common dataset, through generation of a consensus model, leading to increased confidence in the model predictions and underlying models. Using a publicly available dataset, four research groups (NovaMechanics Ltd. (NovaM)-Cyprus, National Technical University of Athens (NTUA)-Greece, QSAR Lab Ltd.-Poland, and DTC Lab-India) built five distinct machine learning (ML) models for the in silico prediction of the zeta potential of metal and metal oxide-nanomaterials (NMs) in aqueous media. The individual models were integrated into a consensus modelling scheme, enhancing their predictive accuracy and reducing their biases. The consensus models outperform the individual models, resulting in more reliable predictions. We propose this approach as a valuable method for increasing the validity of nanoinformatics models and driving regulatory acceptance of in silico new approach methodologies for the use within an “Integrated Approach to Testing and Assessment” (IATA) for risk assessment of NMs.

Organic light-emitting diodes are constantly developed technologically. An advantageous strategy for efficient work of the devices is the design of emitters exhibiting thermally activated delayed fluorescence (TADF). Efforts are invested, especially towards blue TADF fluorophores. The complex nature of the involved photophysical processes prohibits straightforward prediction of dyes with enhanced TADF potential. Design rules are knowledge-based or derived for specific cases. In the present work, we attempt to establish quantitative structure–property relationships (QSPR) between a key optical property of potential TADF emitters and a set of molecular descriptors. The goal is a statistical model providing better understanding of the molecular factors governing the excited states singlet–triplet energy splitting. We utilise multiple linear regression and principal component analysis to correlate the values of the excited singlet–triplet energy gap to various molecular characteristics. The validated models demonstrate the complexity and collective nature of TADF events and highlight the essential role of the electron-donor fragment. [ABSTRACT FROM AUTHOR]

This paper reviews the application of machine learning to the inhibition of corrosion by organic molecules. The methodologies considered include quantitative structure‐property relationships (QSPR) and related data‐driven approaches. The characteristic features of their key components are considered as applied to corrosion inhibition, including datasets, response properties, molecular descriptors, machine learning methods, and structure‐property models. It is shown that the most important factors determining their choice and application features are: (1) the small or very small size of datasets, (2) the mechanism of corrosion inhibition associated with the adsorption of inhibitor molecules on the metal surface, and (3) multifactorial conditioning and noisiness of response property. On this basis, the application of machine learning to the inhibition of corrosion of materials based on iron, aluminum, and magnesium is considered. The main trends in the development of QSPR and related data‐driven modeling of corrosion inhibition are discussed, the shortcomings and common errors are considered, and the prospects for their further development are outlined. [ABSTRACT FROM AUTHOR]

This work is devoted to the investigation of dielectric permittivity which is influenced by electronic, ionic, and dipolar polarization mechanisms, contributing to the material's capacity to store electrical energy. In this study, an extended dataset of 86 polymers was analyzed, and two quantitative structure–property relationship (QSPR) models were developed to predict dielectric permittivity. From an initial set of 1273 descriptors, the most relevant ones were selected using a genetic algorithm, and machine learning models were built using the Gradient Boosting Regressor (GBR). In contrast to Multiple Linear Regression (MLR)- and Partial Least Squares (PLS)-based models, the gradient boosting models excel in handling nonlinear relationships and multicollinearity, iteratively optimizing decision trees to improve accuracy without overfitting. The developed GBR models showed high R2 coefficients of 0.938 and 0.822, for the training and test sets, respectively. An Accumulated Local Effect (ALE) technique was applied to assess the relationship between the selected descriptors—eight for the GB_A model and six for the GB_B model, and their impact on target property. ALE analysis revealed that descriptors such as TDB09m had a strong positive effect on permittivity, while MLOGP2 showed a negative effect. These results highlight the effectiveness of the GBR approach in predicting the dielectric properties of polymers, offering improved accuracy and interpretability. [ABSTRACT FROM AUTHOR]

In this investigation, a quantitative structure-property relationship (QSPR) model coupled with a quantum neural network (QNN) was used to explore the corrosion inhibition efficiency (CIE) of quinoxaline compounds. Integrating quantum chemical properties (QCP) features reduced computational burden by strategically reducing the features from 11 to 4 while maintaining prediction accuracy. QNN models outperform traditional methods like artificial neural networks (ANN) and multilayer perceptron neural networks (MLPNN), with a coefficient of determination (R2) value of 0.987, coupled with diminished root mean square error (RMSE), mean absolute error (MAE), and mean absolute deviation (MAD) values of 0.97, 0.92, and 1.10, respectively. Predictions for six newly synthesized quinoxaline derivatives: quinoxaline-6-carboxylic acid (Q1), methyl quinoxaline-6-carboxylate (Q2), (2E,3E)-2,3-dihydrazono-1,2,3,4-tetrahydroquinoxaline (Q3), (2E,3E) 2,3-dihydrazono-6-methyl-1,2,3,4-tetrahydroquinoxaline (Q4), (E)-3-(4-methoxyethyl)-7-methylquinoxalin-2(1 H)-one (Q5), and 2-(4-methoxyphenyl)-7-methylthieno[3,2-b] quinoxaline (Q6), show remarkable CIE values of 95.12, 96.72, 91.02, 92.43, 89.58, and 93.63 %, respectively. This breakthrough technique simplifies testing and production procedures for new anti-corrosion materials.

Molecular descriptors characterize the biological, physical, and chemical properties of molecules and have long been used for understanding molecular interactions and facilitating materials design. Some of the most robust descriptors are derived from geometrical representations of molecules, called 3-dimensional (3D) descriptors. When calculated from molecular dynamics (MD) simulation trajectories, 3D descriptors can also capture the effects of operating conditions such as temperature or pressure. However, extracting 3D descriptors from MD trajectories is non-trivial, which hinders their wide use by researchers developing advanced quantitative-structure-property-relationship models using machine learning. Here, we describe a suite of open-source Python-based post-processing routines, called PyL3dMD, for calculating 3D descriptors from MD simulations. PyL3dMD is compatible with the popular simulation package LAMMPS and enables users to compute more than 2000 3D molecular descriptors from atomic trajectories generated by MD simulations. PyL3dMD is freely available via GitHub and can be easily installed and used as a highly flexible Python package on all major platforms (Windows, Linux, and macOS). A performance benchmark study used descriptors calculated by PyL3dMD to develop a neural network and the results showed that PyL3dMD is fast and efficient in calculating descriptors for large and complex molecular systems with long simulation durations. PyL3dMD facilitates the calculation of 3D molecular descriptors using MD simulations, making it a valuable tool for cheminformatics studies.

Abstract QSPR mathematically links physicochemical properties with the structure of a molecule. The physicochemical properties of chemical molecules can be predicted using topological indices. It is an effective method for eliminating costly and time-consuming laboratory tests. We established a QSPR between mev-degree and mve-degree-based indices and the physical properties of benzenoid hydrocarbons. To compute these indices, we designed a program using Maple software and the correlation between indices and physical properties was developed using the SPSS software. Our study reveals that the mve-degree-based sum-connectivity $$(\chi ^{mve})$$ ( χ mve ) and atom bond connectivity ( $$ABC^{mve}$$ A B C mve ) index, mev-degree-based Randić ( $$R^{mev}$$ R mev ) and Zagreb ( $$M^{mev}$$ M mev ) index are the three most significant parameters and have good prediction ability for the physicochemical properties. We examined that $$R^{mev}$$ R mev predicts the molar refractivity and boiling point, $${\chi }^{mve}$$ χ mve predicts the LogP and enthalpy, $$ABC^{mve}$$ A B C mve predicts the molecular weight, $$M^{mev}$$ M mev predicts the Gibb’s energy, Pie-electron energy and Henry’s law. Moreover, we computed the indices for the linear [n]-phenylen.

BACKGROUND: Veterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by their retention times (tR) in liquid chromatography–mass spectrometry (LC–MS). One strategy to predict the tR of new veterinary antibiotics is the development of predictive quantitative structure–property relationships (QSPRs), which were used in this study. RESULTS: A database of 122 antibiotics was selected in which the tR was measured using a Hypersil GOLD column. An optimal three‐feature model was developed by integrating the unsupervised variable reduction, replacement method variable subset selection, and multiple linear regression. The negligible differences among the coefficient of determination and the root‐mean‐square error for the training set (R2 = 0.902 and RMSEC = 0.871) and test set (Q2 = 0.854 and RMSEP = 1.064) indicate a stable and predictive model. In a further step, a more in‐depth explanation of the mechanism of action of each descriptor in predicting the tR is provided, with the construction of the theoretical chemical space for accurate predictions of new antibiotics. CONCLUSION: The in silico model developed in this work identified three molecular descriptors associated with aqueous solubility, octanol–water partition coefficient, and the presence of negative and lipophilic atom pairs. The QSPR developed here could be implemented by agricultural and food chemists to identify and monitor existing and new antibiotics within the framework of LC–MS. The computational model was developed in accordance with five principles outlined by the Organization for Economic Co‐operation and Development. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A variational heteroencoder based on recurrent neural networks, trained with SMILES linear notations of molecular structures, was used to derive the following atomic descriptors: delta latent space vectors (DLSVs) obtained from the original SMILES of the whole molecule and the SMILES of the same molecule with the target atom replaced. Different replacements were explored, namely, changing the atomic element, replacement with a character of the model vocabulary not used in the training set, or the removal of the target atom from the SMILES. Unsupervised mapping of the DLSV descriptors with t-distributed stochastic neighbor embedding (t-SNE) revealed a remarkable clustering according to the atomic element, hybridization, atomic type, and aromaticity. Atomic DLSV descriptors were used to train machine learning (ML) models to predict 19F NMR chemical shifts. An R2 of up to 0.89 and mean absolute errors of up to 5.5 ppm were obtained for an independent test set of 1046 molecules with random forests or a gradient-boosting regressor. Intermediate representations from a Transformer model yielded comparable results. Furthermore, DLSVs were applied as molecular operators in the latent space: the DLSV of a halogenation (H→F substitution) was summed to the LSVs of 4135 new molecules with no fluorine atom and decoded into SMILES, yielding 99% of valid SMILES, with 75% of the SMILES incorporating fluorine and 56% of the structures incorporating fluorine with no other structural change. [ABSTRACT FROM AUTHOR]

Ionic liquids (ILs) are considered unique and attractive types of solvents with great potential to capture carbon dioxide (CO2) and reduce its emissions into the atmosphere. On the other hand, carrying out experimental measurements of CO2 solubility for each new IL is time‐consuming and expensive. Whereas, the possible combinations of cations and anions are numerous. Therefore, the preparation and design of such processes requires simple and accurate models to predict the solubility of CO2 as a greenhouse gas. In the present study, two different models, namely: artificial neural network (ANN) and support vector machine optimized with dragonfly algorithm (DA‐SVM) were used in order to establish a quantitative structure–property relationship (QSPR) between the molecular structures of cations and anions and the CO2 solubility. More than 10 116 CO2 solubility data measured in various ionic liquids (ILs) at different temperatures and pressures were collected. 13 significant PaDEL descriptors (E2M, MATS8S, TDB6I, TDB1S, ATSC4V, MATS8M, ATSC7V, Gats2S, Gats5S, Gats5C, ATSC6V, DE, and Lobmax), temperature and pressure were considered as the model input data. For the test set data (2023 data point), the estimated mean absolute error (MAE) and R2 for the ANN model are of 0.0195 and 0.9828 and 0.0219 and 0.9745 for the DA‐SVM model. The results obtained showed that both models can reliably predict the solubility of CO2 in ILs with a slight superiority of the ANN model. Examination of sensitivity and outlier diagnosis examinations confirmed that the QSPR model optimized using the ANN algorithm is better suited to correlate and predict this property. [ABSTRACT FROM AUTHOR]

Amphiphilic peptides hold great potential as drug delivery systems. A popular peptide design approach has been to place amino acids in the peptide sequence based on their known properties. On the other hand, the directed discovery approach aims to screen a sequence space for a desired property. However, screening amphiphilic peptides for desirable drug delivery properties is not possible without a quantity that is predictive of these properties. We studied the predictive power of critical aggregation concentration (CAC) values on the drug delivery performance of a series of amphiphilic peptides with different hydrophobic tails and close CAC values. The CAC values were predicted by our previously developed model and doxorubicin was used as a model hydrophobic drug. All peptides showed close drug loading, entrapment efficiency, and release profile. They also formed similar spherical particles by assembling in reverse β -sheet arrangements regardless of drug presence. Moreover, the assembled particles were able to accumulate doxorubicin inside ordinary as well as drug-resistant breast cancer cells and enhance its toxicity up to 39 and 17 folds, respectively. It can be concluded that similar drug delivery properties displayed by the peptides can be attributed to their similar hydrophilic-lipophilic balance as reflected in their close CAC values. [Display omitted] • The designed amphiphilic peptides co-assembled with doxorubicin and decreased its IC50 in the ordinary and drug-resistant breast cancer cells up to 39 and 17 folds, respectively. • Amphiphilic peptides with different amino acids in their hydrophobic tails and close critical aggregation concentrations showed similar drug delivery performance. • Based on our results critical aggregation concentration can be an estimator of the drug delivery properties of self-assembling amphiphilic peptides. [ABSTRACT FROM AUTHOR]

The study focused on QSPR (Quantitative Structure-Property Relationship) analysis using a variety of topological indices on the medications Mefloquinone, Sertraline, Niclosamide, Tizoxanide, PHA-690509, Ribavirin, Emricasan, and Sofosbuvir. Through the use of computational modeling approaches, the study sought to determine how these medications' chemical structures relate to their individual qualities. The discovered results provided information on the quantitative correlations between structural characteristics and pharmacological qualities, allowing for better comprehension and forecast of their behavior. The results of this study make a positive contribution to the field of medication discovery and design by offering important knowledge about the structure-property correlations of these medicinal molecules. In this article, we focus on using topological indices and a linear regression model to successfully predict various pharmacological features. This method enables more effective drug discovery and development by providing insights into the connection between molecular structure and pharmacological characteristics. We can improve our comprehension of drug behavior and assist targeted drug design by utilizing topological indices and regression analysis. [ABSTRACT FROM AUTHOR]

The quantitative structure-property relationship (QSPR) method is an efficient and elegant method for estimating the critical parameters of a wide range of compounds. In this work, the QSPR data set included the structures of 45 modified diphenyl phosphoryl acetamide ionophores along with their sensitivity to Cd2+, Cu2+, and Pb2+. The data set was divided into the training set, including 36 compounds, and the test set, including 9 compounds. The stepwise -multiple linear regressions (SW-MLR), firefly multiple linear regressions (FA-MLR), and firefly-support vector machine (FA-SVM) models were produced on the training set with sensitivity of ionophores for Cd2+, Cu2+, and Pb2+ for predicting the potentiometric sensitivity of plastic polymer membrane sensors. The FA-SVM model showed good statistical results for all three cations. Internal and external validation was done to ensure the performance of the model. The results showed acceptable accuracy of the proposed method in identifying important descriptors in QSPR. The results of this study and the interpretation of the descriptors entered in the model can help to design new selective ligands. [ABSTRACT FROM AUTHOR]

This study investigates the quantitative structure-property relationship (QSPR) modeling of guar gum biomolecules, focusing on their structural parameters. Guar gum, a polysaccharide with diverse industrial applications, exhibits various properties such as viscosity, solubility, and emulsifying ability, which are influenced by its molecular structure. In this research, M-polynomial and associated topological indices are employed as structural descriptors to represent the molecular structure of guar gum. The M-polynomial and associated topological indices capture important structural features, including size, shape, branching, and connectivity. By correlating these descriptors with experimental data on guar gum properties, predictive models are developed using regression analysis techniques. The analysis revealed a strong correlation between the boiling point and molecular weight and all the considered topological descriptors. The resulting models offer insights into the relationship between guar gum structure and its properties, facilitating the optimization of guar gum production and application in various industries. This study demonstrates the utility of M-polynomial and QSPR modeling in elucidating structure-property relationships of complex biomolecules like guar gum, contributing to the advancement of biomaterial science and industrial applications. [ABSTRACT FROM AUTHOR]

Statistical learning is employed to present a principled framework for the establishment of quantitative structure‐property relationships (QSPR). Property predictions of industrial polymers formed by multiple reagents and at varying molecular weights are focused. A theoretical description of QSPR as well as a rigorous mathematical method is developed for the assimilation of experimental data. Results show that these methods can perform exceptionally well at establishing QSPR for glass transition temperature and intrinsic viscosity of polyesters. [ABSTRACT FROM AUTHOR]

In this study, an artificial neural network (ANN) model was developed based on molecular descriptors to predict the surface tension of liquids. A dataset containing various features was constructed by collecting experimental data from 25 different fluids and extracting molecular structural descriptors. Feature selection was performed using the forward search wrapper method based on Random Forest, identifying 7 significant features (Temperature, MinAbsEStateIndex, LabuteASA, MolMR, Chi1v, qed and FpDensityMorgan3) for surface tension prediction. Subsequently, an ANN model was constructed with the selected features as inputs to predict the surface tension of liquids. The derived model demonstrates high accuracy with a correlation coefficient (R) exceeding 0.999 and a notably low mean square error (MSE = 1.843e−5). Moreover, the ANN model exhibited a total average absolute deviation (AAD) of 0.98 %, comparable to that of the REFPROP, which had a total AAD of 1.26 %. This quantitative model serves an easy tool for gaining insights into the molecular underpinnings of surface tension and predicting its value across various fluids. [ABSTRACT FROM AUTHOR]

Chemical graph theory deals with chemical graphs, which are used to represent chemical systems. Topological indices of molecular graphs are an important area of research in chemical graph theory. These indices are numerical values associated with compounds that aim to establish connections between chemical structures and physical attributes, chemical reactivity, and biological activity. Many graph polynomials have been proposed to compute various topological indices. Quantitative structure-property relationship (QSPR) analysis of chemical compounds involves several regression methods that rely on these topological indices. In this article, we utilize the newly introduced -polynomial to calculate the eccentricity-based indices such as the diameter eccentricity based and hyper diameter eccentricity based index values for nineteen linear saturated monocarboxylic acids. Quantitative structure-property relationship (QSPR) analysis is also performed for the seven thermodynamic properties of monocarboxylic acids. The relationship between thermodynamic properties and topological indices is explored using linear, quadratic and cubic regression models. The best fit curvilinear regression models were determined based on the R² and RMSE values of the models. To further investigate the statistical significance of our models, we also conducted chi-square goodness-of-fit tests to identify the best fit models. [ABSTRACT FROM AUTHOR]

Vegetable oils, which are considered potential lubricants, are composed of different types and proportions of fatty acids. Because of their diverse types and varying compositions, they exhibit different lubrication performances. The genetic function approximation algorithm was used to model the quantitative structure–property relationship between fatty acid structure and the wear scar diameter and friction coefficients measured by four-ball friction and wear tests. Based on the models with adjusted R2 greater than 0.9 and fatty acid compositions of vegetable oils, the wear scar diameter and friction coefficients of Xanthoceras sorbifolia bunge oil and Soybean oil as validation oil samples were predicted. The difference between the predicted and experimental values was small, indicating that the models could accurately predict the lubrication performances of vegetable oils. The lubrication performances of 14 kinds of vegetable oils were predicted by GFA-QSPR models, and the primary factors influencing their lubrication properties were studied by cluster analysis. The results show that the content of C18:1 has a positive effect on the lubrication performances of vegetable oils, while the content of C18:3 has a negative effect, and the length of the carbon chain of fatty acids significantly affects their lubrication properties. [ABSTRACT FROM AUTHOR]

The concept of "topological index" refers to a numerical value determined by the structure of a chemical network. It serves to determine the physicochemical and biological properties of diverse medications, offering a more precise depiction of the theoretical properties of organic materials, this is achieved through the utilization of degree-based topological indices. Because of the development of resistance to existing treatments and the unpleasant side effects associated with some current drugs, the hunt for new drugs remains a priority. In drug discovery, QSPR approaches are widely used to predict, from a chemical structure, the biological activity of potential novel drugs. Researchers can prioritize compounds for synthesis and optimize them to improve potency, preference, and other desired attributes by establishing a correlation between chemical features and biological activity. Rational drug design approaches incorporate research methodologies such as quantitative structureactivity relationships (QSAR) and quantitative structure-property relationships (QSPR), along with decision-making strategies. The goal of these strategies is to improve the biological activity and physicochemical qualities of existing leads. This research includes mathematical modeling of drug mechanisms utilizing multiple-criteria decision analysis and QSPR analysis. Furthermore, using decision-making techniques, I can determine the order of production for various drugs used to treat bone cancer based on their examination using QSPR analysis and topological indices. [ABSTRACT FROM AUTHOR]

The drug design process can be successfully supported using a variety of in silico methods. Some of these are oriented toward molecular property prediction, which is a key step in the early drug discovery stage. Before experimental validation, drug candidates are usually compared with known experimental data. Technically, this can be achieved using machine learning approaches, in which selected experimental data are used to train the predictive models. The proposed Python software is designed for this purpose. It supports the entire workflow of molecular data processing, starting from raw data preparation followed by molecular descriptor creation and machine learning model training. The predictive capabilities of the resulting models were carefully validated internally and externally. These models can be easily applied to new compounds, including within more complex workflows involving generative approaches.