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Separating monomeric cycloalkanes from naphtha obtained from direct coal liquefaction not only facilitates the valuable utilization of naphtha but also holds potential for addressing China's domestic chemical feedstock market demand for these compounds. In extractive distillation processes of naphtha, relative volatility serves as a crucial parameter for extractant selection. However, determining relative volatility through conventional vapor-liquid equilibrium experiments for extractant selection proves challenging due to the complexity of naphtha's compound composition. To address this challenge, a prediction model for the relative volatility of n-heptane/methylcyclohexane in various extractants has been developed using machine-learning quantitative structure-property relationship methods. The model enables rapid and cost-effective extractant selection. The statistical analysis of the model revealed favorable performance indicators, including a coefficient of determination of 0.88, cross-validation coefficient of 0.94, and root mean square error of 0.02. Factors such as α, EHOMO, ρ, and logPoct/water collectively influence relative volatility. Analysis of standardized coefficients in the multivariate linear regression equation identified density as the primary factor affecting the relative volatility of n-heptane/methylcyclohexane in the different extractants. Extractants with higher densities, devoid of branched chains, exhibited increased relative volatility compared to their counterparts with branched chains. Subsequently, the process of separating cycloalkane monomers from direct coal liquefaction products via extractive distillation was optimized using Aspen Plus software, achieving purities exceeding 0.99 and yields exceeding 0.90 for cyclohexane and methylcyclohexane monomers. Economic, energy consumption, and environmental assessments were conducted. Salicylic acid emerged as the most suitable extractant for purifying cycloalkanes in direct coal liquefaction naphtha due to its superior separation effectiveness, cost efficiency, and environmental benefits. The tower parameters of the simulated separation unit provide valuable insights for the design of actual industrial equipment. [ABSTRACT FROM AUTHOR]

AbstractMolecular descriptors are essential tools that bridge the gap between chemical structures and their properties. Among these, the Randić index (R), geometric-arithmetic index (GA), and arithmetic-geometric index (AG) are well-established. However, the relationships between these descriptors, particularly the gaps GA−R and AG−R, have not been extensively explored. This study investigates the impact of these descriptor gaps on chemical graph theory. Our findings reveal that the differences GA−R and AG−R provide unique insights into the structure-property modeling of molecules, particularly outperforming AG, GA, R, and other known descriptors for alkanes. Polycyclic aromatic compounds (PACs) are organic molecules composed of multiple fused aromatic rings. PACs are of significant environmental concern due to their persistence and potential toxicity, including carcinogenic properties. The effectiveness of GA−R and AG−R is observed to be strong in structure-property modeling for some polycyclic aromatic compounds. Additionally, GA−R and AG−R are recognized as important descriptors for various pharmaceutical compounds employed in treating malignant diseases. [ABSTRACT FROM AUTHOR]

The insufficient hazard thresholds of specific individual aromatic hydrocarbon compounds (AHCs) with diverse structures limit their ecological risk assessment. Thus, herein, quantitative structure–activity relationship (QSAR) models for estimating the hazard threshold of AHCs were developed based on the hazardous concentration for 5% of species (HC5) determined using the optimal species sensitivity distribution models and on the molecular descriptors calculated via the PADEL software and ORCA software. Results revealed that the optimal QSAR model, which involved eight descriptors, namely, Zagreb, GATS2m, VR3_Dzs, AATSC2s, GATS2c, ATSC2i, ω, and Vm, displayed excellent performance, as reflected by an optimal goodness of fit (R2adj = 0.918), robustness (Q2LOO = 0.869), and external prediction ability (Q2F1 = 0.760, Q2F2 = 0.782, and Q2F3 = 0.774). The hazard thresholds estimated using the optimal QSAR model were approximately close to the published water quality criteria developed by different countries and regions. The quantitative structure–toxicity relationship demonstrated that the molecular descriptors associated with electrophilicity and topological and electrotopological properties were important factors that affected the risks of AHCs. A new and reliable approach to estimate the hazard threshold of ecological risk assessment for various aromatic hydrocarbon pollutants was provided in this study, which can be widely popularised to similar contaminants with diverse structures. [ABSTRACT FROM AUTHOR]

Drug-induced liver injury (DILI) is a major cause of drug development discontinuation and drug withdrawal from the market, but there are no golden standard methods for DILI risk evaluation. Since we had found the association between DILI and CYP1A1 or CYP1B1 inhibition, we further evaluated the utility of cytochrome P450 (P450) inhibition assay data for DILI risk evaluation using decision tree analysis. The inhibitory activity of drugs with DILI concern (DILI drugs) and no DILI concern (no-DILI drugs) against 10 human P450s was assessed using recombinant enzymes and luminescent substrates. The drugs were also subjected to cytotoxicity assays and high-content analysis using HepG2 cells. Molecular descriptors were calculated by alvaDesc. Decision tree analysis was performed with the data obtained as variables with or without P450-inhibitory activity to discriminate between DILI drugs and no-DILI drugs. The accuracy was significantly higher when P450-inhibitory activity was included. After the decision tree discrimination, the drugs were further discriminated with the P450-inhibitory activity. The results demonstrated that many false-positive and false-negative drugs were correctly discriminated by using the P450 inhibition data. These results suggest that P450 inhibition assay data are useful for DILI risk evaluation. [ABSTRACT FROM AUTHOR]

Background: Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure–property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru. Methods: A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM–RF–GBM model. Results: An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39–0.40 and R2 = 0.73–0.74. Notably, the SVM–RF–GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness. Conclusions: Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts. [ABSTRACT FROM AUTHOR]

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.

We developed a partial least squares regression (PLSR) model based on competitive adaptive reweighted sampling (CARS) to predict the processing factors of 54 pesticides during soybean oil processing. Characteristic variables were selected to improve the performance of the model. Four calculators were used to compute the molecular descriptors used in the model, and the model based on values computed with ChemoPy produced the best results: Rc ​= ​0.94, RMSEc ​= ​0.67, Rp ​= ​0.91, and RMSEp ​= ​0.54 for hot-pressed oil and Rc ​= ​0.93, RMSEc ​= ​0.73, Rp ​= ​0.93, and RMSEp ​= ​0.59 for cold-pressed oil. A rapid and quantitative model of processing factors was established to predict the behaviour and distribution of pesticide residues during food processing. The model was further validated using data from field-grown soybeans; it demonstrated a high correlation coefficient between predicted and measured residue concentrations (Rp ​> ​0.93, RMSEp ​

Abstract Supramolecular chemistry is a fascinating field that explores the interactions between molecules to create higher-order structures. In the case of the supramolecular chain of Fuchsine acid, which is a type of dye molecule, several chemical applications are possible. Fuchsine acid helps to make better medicine carriers that deliver drugs where they’re needed in the body, making treatments more effective and reducing side effects. It also helps create smart materials like sensors and self-fixing plastics, which are useful in electronics, keeping our environment clean, and making new materials. In sensing and detection, the supramolecular chain of Fuchsine acid utilizes as a sensor or detector for specific analyzes. In drug delivery, the supramolecular chains of Fuchsine acid incorporated into drug delivery systems. In recent years, a common method is linking a graph to a chemical structure and using topological descriptors to study it. This technique is becoming increasingly important over time. Topological descriptors gives very useful information while studying the topology of chemical graph. In this paper, we have computed the 3D structure of supramolecular graph of Fuchsine acid. We have computed an explicit expressions of ABC index, GA index, General Randi $$\acute{c}$$ c ´ index, first and second Zagreb index, hyper Zagreb index, H-index and F-index of supramolecular structure of Fushine acid.

Supramolecular chemistry is a fascinating field that explores the interactions between molecules to create higher-order structures. In the case of the supramolecular chain of Fuchsine acid, which is a type of dye molecule, several chemical applications are possible. Fuchsine acid helps to make better medicine carriers that deliver drugs where they’re needed in the body, making treatments more effective and reducing side effects. It also helps create smart materials like sensors and self-fixing plastics, which are useful in electronics, keeping our environment clean, and making new materials. In sensing and detection, the supramolecular chain of Fuchsine acid utilizes as a sensor or detector for specific analyzes. In drug delivery, the supramolecular chains of Fuchsine acid incorporated into drug delivery systems. In recent years, a common method is linking a graph to a chemical structure and using topological descriptors to study it. This technique is becoming increasingly important over time. Topological descriptors gives very useful information while studying the topology of chemical graph. In this paper, we have computed the 3D structure of supramolecular graph of Fuchsine acid. We have computed an explicit expressions of ABC index, GA index, General Randi c ´ index, first and second Zagreb index, hyper Zagreb index, H-index and F-index of supramolecular structure of Fushine acid. [ABSTRACT FROM AUTHOR]

Transmembrane protease serine 2 (TMPRSS2) is an important drug target due to its role in the infection mechanism of coronaviruses including SARS-CoV-2. Current understanding regarding the molecular mechanisms of known inhibitors and insights required for inhibitor design are limited. This study investigates the effect of inhibitor binding on the intramolecular backbone hydrogen bonds (BHBs) of TMPRSS2 using the concept of hydrogen bond wrapping, which is the phenomenon of stabilization of a hydrogen bond in a solvent environment as a result of being surrounded by non-polar groups. A molecular descriptor which quantifies the extent of wrapping around BHBs is introduced for this. First, virtual screening for TMPRSS2 inhibitors is performed by molecular docking using the program DOCK 6 with a Generalized Born surface area (GBSA) scoring function. The docking results are then analyzed using this descriptor and its relationship to the solvent-accessible surface area term ΔGsa of the GBSA score is demonstrated with machine learning regression and principal component analysis. The effect of binding of the inhibitors camostat, nafamostat, and 4-guanidinobenzoic acid (GBA) on the wrapping of important BHBs in TMPRSS2 is also studied using molecular dynamics. For BHBs with a large increase in wrapping groups due to these inhibitors, the radial distribution function of water revealed that certain residues involved in these BHBs, like Gln438, Asp440, and Ser441, undergo preferential desolvation. The findings offer valuable insights into the mechanisms of these inhibitors and may prove useful in the design of new inhibitors. [ABSTRACT FROM AUTHOR]

In cheminformatics, molecular fingerprints (FPs) are used in various tasks such as regression and classification. However, predictive models often underutilize Morgan FP for regression and related tasks in machine learning. This study introduced descriptors derived from reshaped Morgan FPs using persistent homology for the predictive accuracy improvement. In the solvation free energy (FreeSolv) and water solubility (ESOL) datasets, persistent homology was found to enhance predictive accuracy compared to the use of only Morgan FPs. Notably, using the first-order persistence diagram (PD1) for descriptor generation resulted in more significant improvements than using the zeroth-order persistence diagram (PD0). Combining 4096 bits Morgan FPs with PD1-generated descriptors increased the average coefficient of determination in the Gaussian process regression from 0.597 to 0.667 for FreeSolv and from 0.629 to 0.654 for ESOL. Adjusting the grid size parameter during PD-based descriptor generation is crucial, as finer grids, especially with PD0, generate more descriptors but reduce predictive accuracy. Coarsening the grid or applying principal component analysis (PCA) mitigates overfitting and enhances accuracy. When descriptors were generated from Morgan FPs with randomly shuffled bit positions, coarsening the grid and/or applying PCA achieved similar accuracy improvements as when the persistent homology of the original Morgan FPs was used. [ABSTRACT FROM AUTHOR]

We used the extreme gradient boosting (XGB) algorithm to predict the experimental solubility of chemical compounds in water and organic solvents and to select significant molecular descriptors. The accuracy of prediction of our forward stepwise top-importance XGB (FSTI-XGB) on curated solubility data sets in terms of RMSE was found to be 0.59–0.76 Log(S) for two water data sets, while for organic solvent data sets it was 0.69–0.79 Log(S) for the Methanol data set, 0.65–0.79 for the Ethanol data set, and 0.62–0.70 Log(S) for the Acetone data set. That was the first step. In the second step, we used uncurated and curated AquaSolDB data sets for applicability domain (AD) tests of Drugbank, PubChem, and COCONUT databases and determined that more than 95% of studied ca. 500,000 compounds were within the AD. In the third step, we applied conformal prediction to obtain narrow prediction intervals and we successfully validated them using test sets' true solubility values. With prediction intervals obtained in the last fourth step, we were able to estimate individual error margins and the accuracy class of the solubility prediction for molecules within the AD of three public databases. All that was possible without the knowledge of experimental database solubilities. We find these four steps novel because usually, solubility-related works only study the first step or the first two steps. [ABSTRACT FROM AUTHOR]

This article discusses a supervised machine learning-graph theory approach for analyzing the electronic properties of alkanes. The study presents a supervised learning model that predicts the HOMO and LUMO energies of alkanes using molecular topological indices as descriptors. The model utilizes artificial neural networks and support vector machines to take advantage of the correlation between the descriptors. The results show that this supervised learning model outperforms other models in predicting the electronic properties of alkanes. The article emphasizes the importance of selecting appropriate descriptors and learning systems for accurately modeling molecules with topological orbitals. [Extracted from the article]

Background: Peru is one of the most biodiverse countries in the world, which is reflected in its wealth of knowledge about medicinal plants. However, there is a lack of information regarding intestinal absorption and the permeability of natural products. The human colon adenocarcinoma cell line (Caco-2) is an in vitro assay used to measure apparent permeability. This study aims to develop a quantitative structure–property relationship (QSPR) model using machine learning algorithms to predict the apparent permeability of the Caco-2 cell in natural products from Peru. Methods: A dataset of 1817 compounds, including experimental log Papp values and molecular descriptors, was utilized. Six QSPR models were constructed: a multiple linear regression (MLR) model, a partial least squares regression (PLS) model, a support vector machine regression (SVM) model, a random forest (RF) model, a gradient boosting machine (GBM) model, and an SVM–RF–GBM model. Results: An evaluation of the testing set revealed that the MLR and PLS models exhibited an RMSE = 0.47 and R2 = 0.63. In contrast, the SVM, RF, and GBM models showcased an RMSE = 0.39–0.40 and R2 = 0.73–0.74. Notably, the SVM–RF–GBM model demonstrated superior performance, with an RMSE = 0.38 and R2 = 0.76. The model predicted log Papp values for 502 natural products falling within the applicability domain, with 68.9% (n = 346) showing high permeability, suggesting the potential for intestinal absorption. Additionally, we categorized the natural products into six metabolic pathways and assessed their drug-likeness. Conclusions: Our results provide insights into the potential intestinal absorption of natural products in Peru, thus facilitating drug development and pharmaceutical discovery efforts.

Abstract 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. Graphical Abstract

QSPR modeling has been performed on 38 organic peroxides against onset temperature. The molecular structures were optimized and frequencies were computed with DFT/b3lyp and 6-311g(d,p) basis set. Avogadro 1.2 was used to Convert gaussian output files to MDL SDFile format. 5666 molecular descriptors were calculated by Alvadesc software based on MDL SDfiles. The data set is randomly divided into training and test sets, and the number of samples contained in the training and test sets are 30 and 8, respectively. A seven-parameter relationship was established by the stepwise multiple linear regression method. The correlation coefficient reaches 0.9843, and the RMSE for the training and test sets are 5.47 and 5.57, respectively. Student t-value, VIF value and Durbin–Watson value indicate that all the independent variables have significant impact on dependent variable, and there is no multicollinearity between independent variables and no correlation between the residuals, respectively. LOOCV, LMOCV, external validation, Application domain analysis show that the resulting QSPR model is robust and reliable and can be used to predict the thermal decomposition temperature of organic peroxides. [ABSTRACT FROM AUTHOR]

In this study, novel molecular structure encoding descriptors composed of feature encoding and one‐hot encoding was developed and then convolutional autoencoder was used to denoise based on the structure of ionic liquids (ILs). It could be used to predict the CO2 solubility in ILs at different temperatures and pressures, when combined with three different machine learning algorithms (multilayer perceptron [MLP], random forest [RF], and support vector machine [SVM]). Statistics of the prediction results show that the newly proposed molecular structure‐based coding has better regression prediction performance than the conventional molecular cheminformatics descriptors. SE‐MLP model with R2 of 0.9873 and mean square error of 0.0007 has the best performance in predicting the CO2 solubility in ILs. In addition, the relationship between features and dissolved CO2 capacity was analyzed through model interpretation to retrieve physical insights for the underlying system. This work provided a new predictive tool for enriching and refining data on CO2 solubility in ILs and for solving phase equilibrium problems. [ABSTRACT FROM AUTHOR]

This contribution examines the interpretation of the Graph Derivative Indices (GDIs) from the Quantum Mechanics (QM) perspective and its relation with the Hückel molecular orbital (HMO) method. The different elements used for calculating Graph Discrete Derivatives over atom-pairs are related to the QM integrals appearing in the Hückel Determinant. The relation between the Coulomb and Resonance Integrals was estimated by the topological way and quantitative values for resonance energies were calculated. Some GDI calculations were performed to the topological interpretation of the aromaticity, expressing the frequencies as probabilities. Starting from topological interpretation for aromaticity, GDI calculations were performed. There is the possibility of expressing the frequencies as probabilities. It allows explaining the atypical formation of cyclobutadiene from the entropic point of view. Considering this hypothesis, experimental resonance energies for 14 molecules were correlated with GDI-topological resonance energies by the same structures. Taking into consideration the regularity and coherence stablished in experiments performed with the GDIs, it is possible to assure that GDIs have interpretations in QM terms. [ABSTRACT FROM AUTHOR]

Extensive research has accumulated suggesting that Janus kinase 3 (JAK3) is closely related to the occurrence and development of various human diseases, making JAK3 a highly potential drug target. However, JAK3 has high homology with other members of the JAK family, making the development of JAK3 inhibitors full of challenges. Thus, here, a naïve Bayesian classification (NBC) model based on multiple JAK3 protein conformations, which integrates molecular docking, pharmacophore, and molecular descriptors, is developed to find novel JAK3 inhibitors. First, the validation set is used to prove whether molecular docking or pharmacophore, integrating multiple JAK3 conformations always has higher prediction accuracy than that of any single conformation. Second, external prediction reveals that the NBC model combining molecular docking, pharmacophore, and important molecular features could significantly improve the enrichment of active JAK3 inhibitors. Finally, the optimal NBC model is utilized for virtual screening against a large chemical database and some compounds with high Bayesian scores are identified. Altogether, the machine learning‐based virtual screening protocol not only has strong efficiency but also has high screening accuracy. It is hoped that the developed virtual screening strategy could provide valuable guidance for the discovery of novel JAK3 inhibitors. [ABSTRACT FROM AUTHOR]

Molecular descriptors are widely employed to present molecular characteristics in cheminformatics. In QSAR/QSPR study, molecular descriptors are utilized to predict the bioactivity of chemical compounds. The polyphenylene network, known as porous graphene, is one of the most important and widely studied two-dimensional materials. In this paper we compute exact analytical expressions for certain distance based topological indices for polyphenylene superhoneycomb networks. [ABSTRACT FROM AUTHOR]

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected. [ABSTRACT FROM AUTHOR]

Chemical graph theory (CGT) is a field of mathematical chemistry focused on the study of molecules by applying classic graph theory. CGT studies molecules as graphs in which atoms are assumed to be vertices and bonds are taken as edges of graphs. In CGT, topological descriptors predict the physical and chemical properties of any molecular structure. In this paper, we computed some novel topological descriptors viz. Modified version of the Second Zagreb index, Hyper index, Randić index, Modified version of the Forgotten topological index and redefined third Zagreb index for cerium oxide nanostructures ( CeO 2 [ α , β , γ ] ) with α × β unit cell and γ layers. Based on the derived formulas, we compared the numerical results and physio-chemical properties of the under study CeO 2 [ α , β , γ ] . [ABSTRACT FROM AUTHOR]

Despite the availability and accuracy of modern spectroscopic characterization, the utilization of spectral information in chemical machine learning is still primitive. Here, we report an optical character recognition‐based automatic process to utilize spectral information as molecular descriptors, which directly transforms experimental spectrum images to readable vectors. We demonstrate its machine learning application in the reaction yield dataset of Pd‐catalyzed Buchwald‐Hartwig cross‐coupling with aryl halides. In addition, we also show that the predicted spectrum can serve as an alternative encoding source to support the model training. [ABSTRACT FROM AUTHOR]

Solvation Gibbs energy of chemicals is a critical parameter in chemical industry and chemical reactivity. Predicting the solvation Gibbs energies for a large number of solvents and solutes through machine learning techniques is challenging area. In this work, the random forest (RF) algorithm, together with a combined descriptor set from solvents and solutes, was used for developing a quantitative structure–property relationship (QSPR) model for solvation Gibbs energies of 6238 solute/solvent pairs. The optimal RF (ntree = 25, mtry = 10 and nodesize = 5) model was obtained, whose training and test sets, respectively, have determination coefficients of 0.935 and 0.924, and root mean square errors of 2.477 and 2.464 kJ·mol− 1. In predicting the solvation Gibbs energies for a large dataset, the optimal RF model is comparable to other QSPR models reported in the literature. [ABSTRACT FROM AUTHOR]

Oral reference dose (RfD) is a key parameter for deriving the human health ambient water quality criteria (AWQC) for non-carcinogenic substances. In this study, a non-experimental approach was used to calculate the RfD values, which explore the potential correlation between toxicity and physicochemical characteristics and the chemical structure of pesticides. The molecular descriptors of contaminants were calculated using T.E.S.T software from EPA, and a prediction model was developed using a stepwise multiple linear regression (MLR) approaches. Approximately 95% and 85% of the data points differ by less than 10-fold and 5-fold between predicted values and true values, respectively, which improves the efficiency of RfD calculation. The model prediction values have certain reference values in the absence of experimental data, which is beneficial to the advancement of contaminant health risk assessment. In addition, using the prediction model constructed in this manuscript, the RfD values of two pesticide substances in the list of priority pollutants are calculated to derive human health water quality criteria. Furthermore, an initial assessment of the health risk was performed by the quotient value method based on the human health water quality criteria calculated by the prediction model. [ABSTRACT FROM AUTHOR]

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The twisted donor–acceptor (D‐A) organic formwork with a large dihedral angle (θDA) is usually adopted to narrow the singlet‐triplet energy gap for obtaining excellent thermally activated delayed fluorescence (TADF) emitters. However, the dependence of overall TADF properties on θDA has not been systematically investigated to this day. Taking new designed CzBP, CzBP‐1M and CzBP‐2M via introducing methyl as investigated models, it is found that (i) with increasing θDA, the charge transfer component in S1 is larger than that in T1 in varying degrees, leading to non‐monotonic spin‐orbit couplings; (ii) the electron‐vibration couplings between S1 and T1 states become the largest when θDA approaching 80°, facilitating phonon‐driven up‐conversion; (iii) the overall TADF rate reaches a peak at θDA≈80°. By this, the TADF on/off switching is realized via methyl moiety for regulating θDA from theoretical prediction to experimental confirmation. Importantly, the θDA near 80° would be a good descriptor for screening excellent D‐A type TADF emitters. [ABSTRACT FROM AUTHOR]

• Development of models for predicting the heat capacity of liquid organic compounds using machine learning. • Introducing a temperature parameter allows the prediction of the liquid heat capacity at different temperatures. • An automatic process for deriving molecular structural features and heat capacity predictions was realized. • SHAP are used to interpret the constructed model and reveal key features. Heat capacity of compounds is an important reference parameter in thermodynamic, chemical processes, energy storage, heat transfer, heat exchange. However, the heat capacity data of most liquid compounds are missing due to the limited availability of data and the complexity of its experimental measurement. Heat capacity prediction models established did not consider temperature influences, or their complex prediction processes restricted their application. In this work, we developed an automated process based on machine learning algorithms to directly predict the liquid heat capacity of compounds from their molecular structure, with broader applicability to different temperature conditions. A dataset including 1253 heat capacity samples of liquid compounds was collected and divided into training set and test set in a 7:3 ratio. Random Forest (RF), Gradient Boosting Regression Tree (GBRT), Multi-Layer Perception (MLP), and Ridge Regression (RR) algorithms were utilized to develop predictive models. Ten-fold cross-validation, combined with the coefficient of determination (R2), was employed to evaluate the model performance. Notably, GBRT performed the best on the test set with an R 2 of 0.973, an AARD% of 5.248, and an MAE of 9.385 J/mol•K demonstrates the high accuracy of the model, and the model was saved and integrated into an automated prediction process. Meanwhile, through the application of the SHAP method, it was discovered that ATS0pe, NumValenceElectrons, and Kappa2 were identified as the most important features. This study introduces an automated approach for predicting the liquid heat capacities of compounds at various temperatures for the researchers in related fields. [ABSTRACT FROM AUTHOR]

The branching index (also known as the connectivity index), introduced in Milan Randić's seminal paper (J Am Chem Soc 97(23):6609–6615, 1975), is one of the the most famous, investigated, and applied among the graph-theoretical molecular descriptors. The atom-bond connectivity (ABC) index (Estrada et al in Indian J Chem A 37:849–855, 1998) and the sum-connectivity (SC) index (Zhou and Trinajstić in J Math Chem 46:1252–1270, 2009) belong to the class of successful variants of the connectivity index. In the present paper, by amalgamating the core idea of the SC and ABC indices, a new molecular descriptor is put forward—the atom-bond sum-connectivity (ABS) index. The graphs attaining the extreme values of the ABS index are determined over the classes of (molecular) trees and general graphs of a fixed order. A noteworthy property of the ABC index is that it increases when a non-isolated edge is inserted between any two non-adjacent vertices. It is proved that this property holds also for the ABS index. [ABSTRACT FROM AUTHOR]

The spectral graph theory explores connections between combinatorial features of graphs and algebraic properties of associated matrices. The neighborhood inverse sum indeg ( N I ) index was recently proposed and explored to be a significant molecular descriptor. Our aim is to investigate the N I index from a spectral standpoint, for which a suitable matrix is proposed. The matrix is symmetric since it is generated from the edge connection information of undirected graphs. A novel graph energy is introduced based on the eigenvalues of that matrix. The usefulness of the energy as a molecular structural descriptor is analyzed by investigating predictive potential and isomer discrimination ability. Fundamental mathematical properties of the present spectrum and energy are investigated. The spectrum of the bipartite class of graphs is identified to be symmetric about the origin of the real line. Bounds of the spectral radius and the energy are explained by identifying the respective extremal graphs. [ABSTRACT FROM AUTHOR]

Reproductive toxicity is one of the prominent endpoints in the risk assessment of environmental and industrial chemicals. Due to the complexity of the reproductive system, traditional reproductive toxicity testing in animals, especially guideline multigeneration reproductive toxicity studies, take a long time and are expensive. Therefore, machine learning, as a promising alternative approach, should be considered when evaluating the reproductive toxicity of chemicals. We curated rat multigeneration reproductive toxicity testing data of 275 chemicals from ToxRefDB (Toxicity Reference Database) and developed predictive models using seven machine learning algorithms (decision tree, decision forest, random forest, k-nearest neighbors, support vector machine, linear discriminant analysis, and logistic regression). A consensus model was built based on the seven individual models. An external validation set was curated from the COSMOS database and the literature. The performances of individual and consensus models were evaluated using 500 iterations of 5-fold cross-validations and the external validation data set. The balanced accuracy of the models ranged from 58% to 65% in the 5-fold cross-validations and 45%-61% in the external validations. Prediction confidence analysis was conducted to provide additional information for more appropriate applications of the developed models. The impact of our findings is in increasing confidence in machine learning models. We demonstrate the importance of using consensus models for harnessing the benefits of multiple machine learning models (i.e., using redundant systems to check validity of outcomes). While we continue to build upon the models to better characterize weak toxicants, there is current utility in saving resources by being able to screen out strong reproductive toxicants before investing in vivo testing. The modeling approach (machine learning models) is offered for assessing the rat multigeneration reproductive toxicity of chemicals. Our results suggest that machine learning may be a promising alternative approach to evaluate the potential reproductive toxicity of chemicals. [ABSTRACT FROM AUTHOR]

Density functional theory calculations on saturated monobasic ketones, aldehydes, and ethers were carried out using Gaussian‐16 software. For each compound, the most stable molecule with the lowest energy and no imaginary frequency was found, and the molecular descriptors were calculated using the Alvadesc software based on the optimized geometry. The stepwise MLR method was used to develop a four‐parameter linear regression equation between the critical temperatures and molecular descriptors of 50 compounds in the training set, with a correlation coefficient of.9991. The QSPR model was robust and reliable, according to the Fisher and student‐t tests, the Durbin‐Watson test, cross‐validation and external validation, and application domain analysis. [ABSTRACT FROM AUTHOR]

The manufacturing of pharmaceutical solid dosage forms, such as tablets involves a large number of successive processing operations including crystallisation of the drug substance, granulation, drying, milling, mixing of the formulation, and compaction. Each step is fraught with manufacturing problems. Undesired adhesion of powders to the surface of the compaction tooling, known as sticking, is a frequent and highly disruptive problem that occurs at the very end of the process chain when the tablet is formed. As an alternative to the mechanistic approaches to address sticking, we introduce two different machine learning strategies to predict sticking directly from the chemical formula of the drug substance, represented by molecular descriptors. An empirical database for sticking behaviour was developed and used to train the machine learning (ML) algorithms to predict sticking properties from molecular descriptors. The ML model has successfully classified sticking/non-sticking behaviour of powders with 100% separation. Predictions were made for materials in the handbook of Pharmaceutical Excipients and a subset of molecules included in the ChemBL database, demonstrating the potential use of machine learning approaches to screen for sticking propensity early at drug discovery and development stages. This is the first-time molecular descriptors and machine learning were used to predict and screen for sticking behaviour. The method has potential to transform the development of medicines by providing manufacturability information at drug screening stage and is potentially applicable to other manufacturing problems controlled by the chemistry of the drug substance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Virtual screening can significantly save experimental time and costs for early drug discovery. Drug multi-classification can speed up virtual screening and quickly predict the most likely class for a drug. In this study, 1019 drug molecules with actual therapeutic effects are collected from multiple databases and documents, and molecular sets are grouped according to therapeutic effect and mechanism of action. Molecular descriptors and molecular fingerprints are obtained through SMILES to quantify molecular structures. After using the Kennard–Stone method to divide the data set, a better combination can be obtained by comparing the combined results of five classification algorithms and a fusion method. Furthermore, for a specific data set, the model with the best performance is used to predict the validation data set. The test set shows that prediction accuracy can reach 0.862 and kappa coefficient can reach 0.808. The highest classification accuracy of the validation set is 0.873. The more reliable molecular set has been found, which could be used to predict potential attributes of unknown drug compounds and even to discover new use for old drugs. We hope this research can provide a reference for virtual screening of multiple classes of drugs at the same time in the future. [ABSTRACT FROM AUTHOR]

In theoretical chemical sciences, the different versions of the Atom-Bond Connectivity (ABC) indices are used in the statistical analysis of drug molecular structures which are helpful for chemical and medical scientists to find out the chemical and biological characteristics of drugs. In this article, we compute and analyze the different versions of the ABC indices of some significant molecular structures of drugs which are applied to test the medicinal and pharmaceutical characteristics, such as calculated 50% inhibitory concentration IC50 values and Half maximal effective concentration values for the Treatment and Prevention of COVID-19. [ABSTRACT FROM AUTHOR]

A molecular descriptor is a real valued number related to a molecular graph and is a structural invariant. Molecular Descriptors(Topological Indices) are the significant numerical quantities in the fields of chemical graph theory. Quantitative structure-property relationships (QSPR)and structure-activity relationships (SAR) of the polycyclic aromatic hydrocarbon compounds necessitate expressions for the molecular topological features of these compounds. Topological indices are vital devices for investigating chemical compounds to comprehend the fundamental topology of chemical structures. Ev-degree and ve-degree based topological indices are two novel degrees based indices as of late defined in graph theory. Ev-degree and ve-degree based molecular descriptor (topological indices) have been defined as corresponding to their relating partners. In this paper, we have computed topological indices based on ev-degree and ve-degree for the Polycyclic Aromatic Hydrocarbon (P A H n). [ABSTRACT FROM AUTHOR]

BackgroundHierarchical cluster analysis (HCA) is a widely used classificatory technique in many areas of scientific knowledge. Applications usually yield a dendrogram from an HCA run over a given data set, using a grouping algorithm and a similarity measure. However, even when such parameters are fixed, ties in proximity (i.e. two equidistant clusters from a third one) may produce several different dendrograms, having different possible clustering patterns (different classifications). This situation is usually disregarded and conclusions are based on a single result, leading to questions concerning the permanence of clusters in all the resulting dendrograms; this happens, for example, when using HCA for grouping molecular descriptors to select that less similar ones in QSAR studies.ResultsRepresenting dendrograms in graph theoretical terms allowed us to introduce four measures of cluster frequency in a canonical way, and use them to calculate cluster frequencies over the set of all possible dendrograms, taking all ties in proximity into account. A toy example of well separated clusters was used, as well as a set of 1666 molecular descriptors calculated for a group of molecules having hepatotoxic activity to show how our functions may be used for studying the effect of ties in HCA analysis. Such functions were not restricted to the tie case; the possibility of using them to derive cluster stability measurements on arbitrary sets of dendrograms having the same leaves is discussed, e.g. dendrograms from variations of HCA parameters. It was found that ties occurred frequently, some yielding tens of thousands of dendrograms, even for small data sets.ConclusionsOur approach was able to detect trends in clustering patterns by offering a simple way of measuring their frequency, which is often very low. This would imply, that inferences and models based on descriptor classifications (e.g. QSAR) are likely to be biased, thereby requiring an assessment of their reliability. Moreover, any classification of molecular descriptors is likely to be far from unique. Our results highlight the need for evaluating the effect of ties on clustering patterns before classification results can be used accurately.Graphical abstractFour cluster contrast functions identifying statistically sound clusters within dendrograms considering ties in proximity.

The screening of compounds for ADME-Tox targets plays an important role in drug design. QSPR models can increase the speed of these specific tasks, although the performance of the models highly depends on several factors, such as the applied molecular descriptors. In this study, a detailed comparison of the most popular descriptor groups has been carried out for six main ADME-Tox classification targets: Ames mutagenicity, P-glycoprotein inhibition, hERG inhibition, hepatotoxicity, blood–brain-barrier permeability, and cytochrome P450 2C9 inhibition. The literature-based, medium-sized binary classification datasets (all above 1,000 molecules) were used for the model building by two common algorithms, XGBoost and the RPropMLP neural network. Five molecular representation sets were compared along with their joint applications: Morgan, Atompairs, and MACCS fingerprints, and the traditional 1D and 2D molecular descriptors, as well as 3D molecular descriptors, separately. The statistical evaluation of the model performances was based on 18 different performance parameters. Although all the developed models were close to the usual performance of QSPR models for each specific ADME-Tox target, the results clearly showed the superiority of the traditional 1D, 2D, and 3D descriptors in the case of the XGBoost algorithm. It is worth trying the classical tools in single model building because the use of 2D descriptors can produce even better models for almost every dataset than the combination of all the examined descriptor sets.

The recent discovery of various forms of carbon nanostructures inspired the researchers due to their uses in various fields. Numerous production approaches for carbon nanostructures have been presented; doping, functionalization, chemical modification, and filling have been achieved, and manipulation, characterization and separation of carbon nanostructures are now possible. On the other hand, the flexibility and strength of carbon nanosheets make them potential candidates in controlling further nanoscale structures, which indicates that they have an important role in nanotechnology engineering. Because of their uniform thickness of only around 1 nanometer(nm), and their high mechanical, thermal and chemical stability, such materials are of high concern for a wide range of applications.A topological index or molecular descriptor is a mathematical formulation that can be applied to any graph which models some molecular structure. Based on the molecular descriptor, it is convenient to analyze mathematical values and advance investigation on some physicochemical properties of a molecule. Consequently, it is an effective method in replacing laborious, expensive and time-consuming laboratory experimentations. Molecular descriptors perform an important part in mathematical chemistry, especially during the investigation of Quantitative structure property relationships (QSPRs) and Quantitative structure-activity relationships (QSARs) that provide insight into animate effects based on chemical structures. In this paper, we have computed some novel neighborhood version molecular descriptors for the two carbon nanosheets VC5C7p,q and (HC5C7p,q) and derived formulas for them. Our computed results are surely helpful in understanding the topology of understudy nanosheets. These computed indices have best correlation with acentric factor and entropy, therefore, are effective in QSPRs and QSARs analysis with powerful accuracy.

Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.

Oral reference dose (RfD) is a key parameter for deriving the human health ambient water quality criteria (AWQC) for non-carcinogenic substances. In this study, a non-experimental approach was used to calculate the RfD values, which explore the potential correlation between toxicity and physicochemical characteristics and the chemical structure of pesticides. The molecular descriptors of contaminants were calculated using T.E.S.T software from EPA, and a prediction model was developed using a stepwise multiple linear regression (MLR) approaches. Approximately 95% and 85% of the data points differ by less than 10-fold and 5-fold between predicted values and true values, respectively, which improves the efficiency of RfD calculation. The model prediction values have certain reference values in the absence of experimental data, which is beneficial to the advancement of contaminant health risk assessment. In addition, using the prediction model constructed in this manuscript, the RfD values of two pesticide substances in the list of priority pollutants are calculated to derive human health water quality criteria. Furthermore, an initial assessment of the health risk was performed by the quotient value method based on the human health water quality criteria calculated by the prediction model.

Machine learning (ML) methods enable computers to address problems by learning from existing data. Such applications are becoming commonplace in molecular sciences. Interest in applying ML techniques across chemical compound space, from predicting properties to designing molecules and materials is in the surge. Especially, ML models have started to accelerate computational chemistry, and are often as accurate as state‐of‐the‐art electronic/atomistic models. Being an integral part of the ML architecture, representation of a molecular entity, uniquely encoded, plays a crucial role to what extent an ML model would be accurately predicting the desired property. This review aims to demonstrate a hierarchy of representations which has been introduced, to capture all degrees of freedom of a molecule or an atom the best, to map the quantum mechanical properties. We discuss their diverse applications how they have been instrumental in harnessing the growing field of ML accelerated computational modeling. [ABSTRACT FROM AUTHOR]

Semi-empirical calculations were employed to understand the effects of introducing promoters such as molybdenum (Mo) and tungsten (W) on chromium (III) oxide catalyst for the dehydrogenation of propane into propylene. For this purpose, we investigated CrM-oxide (M = Cr, Mo, and W) catalysts. In this study, the Lewis acidity of the catalyst was examined using Lewis acidity parameters (Ac), including ammonia and pyridine adsorption energy. The results obtained from this study of overall acidity across all sites of the catalysts studied reveal Mo-modified catalyst as the one with the least acidity while the W-modified catalyst was found to have shown the highest acidity signifies that the introduction of Mo would reduce acidity while W accelerates it. The finding, therefore, confirms tungsten (W) to be more influential and would be more promising when compared to molybdenum (Mo) due to the better avenue that is offered by W for the promotion of electron exchange and its higher acidity(s). The suitability of some molecular descriptors for acidity prediction as a potential alternative to the current use of adsorption energies of the probes was also reported.

Abstract Computer-aided research on the relationship between molecular structures of natural compounds (NC) and their biological activities have been carried out extensively because the molecular structures of new drug candidates are usually analogous to or derived from the molecular structures of NC. In order to express the relationship physically realistically using a computer, it is essential to have a molecular descriptor set that can adequately represent the characteristics of the molecular structures belonging to the NC’s chemical space. Although several topological descriptors have been developed to describe the physical, chemical, and biological properties of organic molecules, especially synthetic compounds, and have been widely used for drug discovery researches, these descriptors have limitations in expressing NC-specific molecular structures. To overcome this, we developed a novel molecular fingerprint, called Natural Compound Molecular Fingerprints (NC-MFP), for explaining NC structures related to biological activities and for applying the same for the natural product (NP)-based drug development. NC-MFP was developed to reflect the structural characteristics of NCs and the commonly used NP classification system. NC-MFP is a scaffold-based molecular fingerprint method comprising scaffolds, scaffold-fragment connection points (SFCP), and fragments. The scaffolds of the NC-MFP have a hierarchical structure. In this study, we introduce 16 structural classes of NPs in the Dictionary of Natural Product database (DNP), and the hierarchical scaffolds of each class were calculated using the Bemis and Murko (BM) method. The scaffold library in NC-MFP comprises 676 scaffolds. To compare how well the NC-MFP represents the structural features of NCs compared to the molecular fingerprints that have been widely used for organic molecular representation, two kinds of binary classification tasks were performed. Task I is a binary classification of the NCs in commercially available library DB into a NC or synthetic compound. Task II is classifying whether NCs with inhibitory activity in seven biological target proteins are active or inactive. Two tasks were developed with some molecular fingerprints, including NC-MFP, using the 1-nearest neighbor (1-NN) method. The performance of task I showed that NC-MFP is a practical molecular fingerprint to classify NC structures from the data set compared with other molecular fingerprints. Performance of task II with NC-MFP outperformed compared with other molecular fingerprints, suggesting that the NC-MFP is useful to explain NC structures related to biological activities. In conclusion, NC-MFP is a robust molecular fingerprint in classifying NC structures and explaining the biological activities of NC structures. Therefore, we suggest NC-MFP as a potent molecular descriptor of the virtual screening of NC for natural product-based drug development.