Quantitative Correlation of Physical and Chemical Properties with Chemical Structure: Utility for Prediction

Degradation of β-lactam antibiotics by Fe(III)/HSO3- system and their quantitative structure-activity relationship

β-lactam antibiotics, extensively used worldwide, pose significant risks to human health and ecological safety due to their accumulation in the environment. Recent studies have demonstrated the efficacy of transition metal-activated sulfite systems, like Fe(Ⅲ)/HSO3-, in removing PPCPs from water. However, research on their capability to degrade β-lactam antibiotics remains sparse. This paper evaluates the degradation of 14 types of β-lactam antibiotics in Fe(Ⅲ)/HSO3- system and establishes a QSAR model correlating molecular descriptors with degradation rates using the MLR method. Using cefazolin as a case study, this research predicts degradation pathways through NPA charge and Fukui function analysis, corroborated by UPLC-MS product analysis. The investigation further explores the influence of variables such as HSO3- dosage, substrate concentration, Fe(Ⅲ) dosage, initial pH and the presence of common seen water matrices including humic acid and bicarbonate on the degradation efficiency. Optimal conditions for cefazolin degradation in Fe(Ⅲ)/HSO3- system were determined to be 93.3 μM HSO3-, 8.12 μM Fe(Ⅲ) and an initial pH of 3.61, under which the interaction of Fe(Ⅲ) dosage with initial pH was found to significantly affect the degradation efficiency. This study not only provides a novel degradation approach for β-lactam antibiotics but also expands the theoretical application horizon of the Fe(Ⅲ)/HSO3- system.

Development and construction of a cost-effective non-contact instrument for measuring the dielectric constant of liquids

This research presents the development and construction of a cost-effective instrument, designed to measure the dielectric constant of liquids by employing a non-contact method that relies on determining the capacitance of a cell containing the liquid and its relaxation frequency. This instrument utilizes an astable multi-vibrator integrated with a resistance-capacitor network, in which the cell housing the liquid of interest functions as a capacitor element of the oscillator. The frequency of the generated oscillations is meticulously recorded using a seven-digit frequency meter with a resolution of 1 Hz. The cell was filled with an array of pure liquids with known dielectric constants, and their frequencies were subsequently recorded at ambient temperatures. An equation was fitted to the frequency-dielectric constant curve, which was used as a calibration equation to determine the dielectric constant of subsequent liquids. In addition to pure liquids, dielectric constants for solvent mixtures of varying mole fractions were also calculated using the previously established calibration equation. Our results demonstrated excellent frequency stability of the instrument, and the obtained dielectric constant values displayed significant consistency with both the experimental data and predictions made by computational methodologies. This suggests that the constructed instrument exhibits a high level of accuracy in measuring the dielectric constant of both pure and mixed liquids, establishing its potential utility in relevant research and industrial applications.

Site-specific template generative approach for retrosynthetic planning

Retrosynthesis, the strategy of devising laboratory pathways by working backwards from the target compound, is crucial yet challenging. Enhancing retrosynthetic efficiency requires overcoming the vast complexity of chemical space, the limited known interconversions between molecules, and the challenges posed by limited experimental datasets. This study introduces generative machine learning methods for retrosynthetic planning. The approach features three innovations: generating reaction templates instead of reactants or synthons to create novel chemical transformations, allowing user selection of specific bonds to change for human-influenced synthesis, and employing a conditional kernel-elastic autoencoder (CKAE) to measure the similarity between generated and known reactions for chemical viability insights. These features form a coherent retrosynthetic framework, validated experimentally by designing a 3-step synthetic pathway for a challenging small molecule, demonstrating a significant improvement over previous 5-9 step approaches. This work highlights the utility and robustness of generative machine learning in addressing complex challenges in chemical synthesis.

Temperature-Dependent Density and Viscosity Prediction for Hydrocarbons: Machine Learning and Molecular Dynamics Simulations

Machine learning-based predictive models allow rapid and reliable prediction of material properties and facilitate innovative materials design. Base oils used in the formulation of lubricant products are complex hydrocarbons of varying sizes and structure. This study developed Gaussian process regression-based models to accurately predict the temperature-dependent density and dynamic viscosity of 305 complex hydrocarbons. In our approach, strongly correlated/collinear predictors were trimmed, important predictors were selected by least absolute shrinkage and selection operator (LASSO) regularization and prior domain knowledge, hyperparameters were systematically optimized by Bayesian optimization, and the models were interpreted. The approach provided versatile and quantitative structure-property relationship (QSPR) models with relatively simple predictors for determining the dynamic viscosity and density of complex hydrocarbons at any temperature. In addition, we developed molecular dynamics simulation-based descriptors and evaluated the feasibility and versatility of dynamic descriptors from simulations for predicting the material properties. It was found that the models developed using a comparably smaller pool of dynamic descriptors performed similarly in predicting density and viscosity to models based on many more static descriptors. The best models were shown to predict density and dynamic viscosity with coefficient of determination (R2) values of 99.6% and 97.7%, respectively, for all data sets, including a test data set of 45 molecules. Finally, partial dependency plots (PDPs), individual conditional expectation (ICE) plots, local interpretable model-agnostic explanation (LIME) values, and trimmed model R2 values were used to identify the most important static and dynamic predictors of the density and viscosity.

Prediction of critical micelle concentration for per- and polyfluoroalkyl substances

In this study, we focus on the development of Quantitative Structure-Property Relationship (QSPR) models to predict the critical micelle concentration (CMC) for per- and polyfluoroalkyl substances (PFASs). Experimental CMC values for both fluorinated and non-fluorinated compounds were meticulously compiled from existing literature sources. Our approach involved constructing two distinct types of models based on Support Vector Machine (SVM) algorithms applied to the dataset. Type (I) models were trained exclusively on CMC values for fluorinated compounds, while Type (II) models were developed utilizing the entire dataset, incorporating both fluorinated and non-fluorinated compounds. Comparative analyses were conducted against reference data, as well as between the two model types. Encouragingly, both types of models exhibited robust predictive capabilities and demonstrated high reliability. Subsequently, the model having the broadest applicability domain was selected to complement the existing experimental data, thereby enhancing our understanding of PFAS behaviour.

Comment on Naef, R.; Acree, W.E., Jr. Calculation of the Three Partition Coefficients logPow, logKoa and logKaw of Organic Molecules at Standard Conditions at Once by Means of a Generally Applicable Group Additivity Method. Preprints, 2023120275

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Descriptors of water aggregation

We rely on a total of 23 (cluster size, 8 structural, and 14 connectivity) descriptors to investigate structural patterns and connectivity motifs associated with water cluster aggregation. In addition to the cluster size n (number of molecules), the 8 structural descriptors can be further categorized into (i) one-body (intramolecular): covalent OH bond length (rOH) and HOH bond angle (θHOH), (ii) two-body: OO distance (rOO), OHO angle (θOHO), and HOOX dihedral angle (ϕHOOX), where X lies on the bisector of the HOH angle, (iii) three-body: OOO angle (θOOO), and (iv) many-body: modified tetrahedral order parameter (q) to account for two-, three-, four-, five-coordinated molecules (qm, m = 2, 3, 4, 5) and radius of gyration (Rg). The 14 connectivity descriptors are all many-body in nature and consist of the AD, AAD, ADD, AADD, AAAD, AAADD adjacencies [number of hydrogen bonds accepted (A) and donated (D) by each water molecule], Wiener index, Average Shortest Path Length, hydrogen bond saturation (% HB), and number of non-short-circuited three-membered cycles, four-membered cycles, five-membered cycles, six-membered cycles, and seven-membered cycles. We mined a previously reported database of 4 948 959 water cluster minima for (H2O)n, n = 3-25 to analyze the evolution and correlation of these descriptors for the clusters within 5 kcal/mol of the putative minima. It was found that rOH and % HB correlated strongly with cluster size n, which was identified as the strongest predictor of energetic stability. Marked changes in the adjacencies and cycle count were observed, lending insight into changes in the hydrogen bond network upon aggregation. A Principal Component Analysis (PCA) was employed to identify descriptor dependencies and group clusters into specific structural patterns across different cluster sizes. The results of this study inform our understanding of how water clusters evolve in size and what appropriate descriptors of their structural and connectivity patterns are with respect to system size, stability, and similarity. The approach described in this study is general and can be easily extended to other hydrogen-bonded systems.

Viscosity of Ionic Liquids: Theories and Models

Ionic liquids (ILs) offer a wide range of promising applications due to their unique and designable properties compared to conventional solvents. Further development and application of ILs require correlating/predicting their pressure-viscosity-temperature behavior. In this review, we firstly introduce methods for calculation of thermodynamic inputs of viscosity models. Next, we introduce theories, theoretical and semi-empirical models coupling various theories with EoSs or activity coefficient models, and empirical and phenomenological models for viscosity of pure ILs and IL-related mixtures. Our modelling description is followed immediately by model application and performance. Then, we propose simple predictive equations for viscosity of IL-related mixtures and systematically compare performances of the above-mentioned theories and models. In concluding remarks, we recommend robust predictive models for viscosity at atmospheric pressure as well as proper and consistent theories and models for P-η-T behavior. The work that still remains to be done to obtain the desired theories and models for viscosity of ILs and IL-related mixtures is also presented. The present review is structured from pure ILs to IL-related mixtures and aims to summarize and quantitatively discuss the recent advances in theoretical and empirical modelling of viscosity of ILs and IL-related mixtures.

General Equation to Estimate the Physicochemical Properties of Aliphatic Amines

Changes in various physicochemical properties (P(n)) of aliphatic amines (including primary, secondary, and tertiary amines) can be roughly divided into nonlinear (P(n)) and linear (PLC(n)) changes. In our previous paper, nonlinear and linear change properties of noncyclic alkanes all were correlated with four parameters, n, SCNE, ΔAOEI, and ΔAIMPI, indicating number of carbon atoms, sum of carbon number effects, average odd-even index difference, and average inner molecular polarizability index difference, respectively. To date, there has been no general equation to express changes in the properties of substituted alkanes. This work, based on the molecular structure characteristics of aliphatic amine molecules, proposes a general equation to express nonlinear changes in their physicochemical properties, named as the "NPAA equation" (eq 12), ln(P(n)) = a + b(n) + c(SCNE) + d(ΔAOEI) + e(PEI) + f(APEI) + g(GN), and proposes a general equation to express linear changes in the physicochemical properties of them, named as the "LPAA equation" (eq 13), PLC(n) = a + b(n) + c(SCNE) + d(ΔAOEI) + e(PEI) + f(APEI) + g(GN). In NPAA and LPAA equations, a, b, c, d, e, f, and g are coefficients, and PEI, APEI, and GN represent the polarizability effect index, average polarizability effect index, and N atomic influence factor, respectively. The results show that nonlinear and linear change properties of aliphatic amines all can be correlated with six parameters, n, SCNE, ΔAOEI, PEI, APEI, and GN. NPAA and LPAA equations have the advantages of uniform expression, high estimation accuracy, and usage of fewer parameters. Further, by employing the above six parameters, a quantitative correlation equation can be established between any two properties of aliphatic amines. Using the obtained equations as model equations, the property data of aliphatic amines were predicted, involving 107 normal boiling points, 10 refractive indexes, 11 liquid densities, 54 critical temperatures, 54 critical pressures, 62 liquid thermal conductivities, 59 surface tensions, 56 heat capacities, 55 critical volumes, 54 gas enthalpies of formation, and 57 gas Gibbs energies of formation, a total of 579 values, which have not been experimentally determined yet. This work not only provides a simple and convenient method for estimating or predicting the properties of aliphatic amines but can also provide new perspectives for quantitative structure-property relationships of substituted alkanes.

The Calculation of Both Electrostatic and Van der Waals Effects to Probe the Efficiency of Solvent Extraction of Heterocyclic Aromatics from Heavy Oil

Due to the complex composition and similar structure, the extraction denitrification of aromatic rich oil is faced with the contradiction problem of denitrification efficiency and aromatic loss which cannot be efficiently solved by experiments. However, the complex interactions involved can be analyzed from the perspective of calculation, and the prediction criteria and methods are proposed. Based on rigorous density functional theory calculation data, Simple models based on electrostatic potential (ESP) and Van der Waals potential (VdWP)-based calculations were established and validated. The twofold model provided the best prediction for interactions between extractants and nitrogen compounds and between extractants and aromatics, which determines denitrification efficiency and aromatic loss, respectively, due to the most complete description of both electrostatic and VdW force. This provides a powerful tool for evaluating the non-covalent interactions and thence tuning the efficiency of the separation process. Thus, high denitrification efficiency (43.2~66.3 %) and moderate aromatic loss (1.7~4.4 %) were obtained using screened deep eutectic solvents (DESs). This ideal observation provided the potential for mild hydrodesulfurization and manufacture of high-grade carbon materials.

Property Prediction and Structural Feature Extraction of Polyimide Materials Based on Machine Learning

The construction of material prediction models using machine learning algorithms can aid in the polyimide structural design and screening of materials as well as accelerate the development of new materials. There is a lack of research on predicting the optical properties of polyimide materials and the interpretation of the structural features. Here, we collected 652 polyimide molecular structures and used seven popular machine learning algorithms to predict the glass transition temperature and cut-off wavelength of polyimide materials and extract key feature information of repeating unit structures. The results showed that the root mean square error of the glass transition temperature prediction model was 33.92 °C, and the correlation coefficient was 0.861. The root mean square error of the cut-off wavelength prediction model was 17.18 nm, and the correlation coefficient was 0.837. The elasticity of the molecular structure was also found to be the key factor affecting glass transition temperature, and the presence and location of heterogeneous atoms had a significant effect on the cut-off wavelengths. Finally, eight polyimide materials were synthesized to test the accuracy of the prediction models, and the experimental characterization values agreed with the predicted values. The results would contribute to the development of polyimide structural design and materials preparation for flexible display.

Combined DFT and Machine Learning Study of the Dissociation and Migration of H in Pyrrole Derivatives

Systematic DFT calculations of model coal-pyrrole derivatives substituted by different functional groups are carried out. The N-H bond dissociation energies (N-H BDEs) and H-transfer activation energies (H-TAEs) of pyrrole derivatives are fully evaluated to elucidate the effect of the type of substituents and their position on the molecular reactivity. The results indicate that compounds substituted with electron-donating groups (EDGs) are more prone to pyrolysis while those substituted with electron-withdrawing groups (EWGs) are difficult to pyrolyze. Furthermore, quantitative structure-property relationship (QSPR) models for N-H BDEs and H-TAEs about pyrrole derivatives are built with multiple linear regression (MLR) and support vector machine (SVM). The final results show that the SVM-QSPR model has better fitness, prediction, and robustness, while the MLR-QSPR model can express the physical meaning better. The effects of functional groups on pyrolysis are clarified by the models presented in this paper, which will support the optimization of ultra-low NOx combustion.

PyL3dMD: Python LAMMPS 3D molecular descriptors package

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.

Machine learning-based models for accessing thermal conductivity of liquids at different temperature conditions

Combating global warming-related climate change demands prompt actions to reduce greenhouse gas emissions, particularly carbon dioxide. Biomass-based biofuels represent a promising alternative fossil energy source. To convert biomass into energy, numerous conversion processes are performed at high pressure and temperature conditions, and the design and dimensioning of such processes requires thermophysical property data, particularly thermal conductivity, which are not always available in the literature. In this paper, we proposed the application of Chemoinformatics methodologies to investigate the prediction of thermal conductivity for hydrocarbons and oxygenated compounds. A compilation of experimental data followed by a careful data curation were performed to establish a database. The support vector machine algorithm has been applied to the database leading to models with good predictive abilities. The support vector regression (SVR) model has then been applied to an external set of compounds, i.e. not considered during the training of models. It showed that our SVR model can be used for the prediction of thermal conductivity values for temperatures and/or compounds that are not covered experimentally in the literature.

Digital Transformation in Toxicology: Improving Communication and Efficiency in Risk Assessment

Toxicology is undergoing a digital revolution, with mobile apps, sensors, artificial intelligence (AI), and machine learning enabling better record-keeping, data analysis, and risk assessment. Additionally, computational toxicology and digital risk assessment have led to more accurate predictions of chemical hazards, reducing the burden of laboratory studies. Blockchain technology is emerging as a promising approach to increase transparency, particularly in the management and processing of genomic data related with food safety. Robotics, smart agriculture, and smart food and feedstock offer new opportunities for collecting, analyzing, and evaluating data, while wearable devices can predict toxicity and monitor health-related issues. The review article focuses on the potential of digital technologies to improve risk assessment and public health in the field of toxicology. By examining key topics such as blockchain technology, smoking toxicology, wearable sensors, and food security, this article provides an overview of how digitalization is influencing toxicology. As well as highlighting future directions for research, this article demonstrates how emerging technologies can enhance risk assessment communication and efficiency. The integration of digital technologies has revolutionized toxicology and has great potential for improving risk assessment and promoting public health.

Updates on drug designing approach through computational strategies: a review

The drug discovery and development (DDD) process in pursuit of novel drug candidates is a challenging procedure requiring lots of time and resources. Therefore, computer-aided drug design (CADD) methodologies are used extensively to promote proficiency in drug development in a systematic and time-effective manner. The point in reference is SARS-CoV-2 which has emerged as a global pandemic. In the absence of any confirmed drug moiety to treat the infection, the science fraternity adopted hit and trial methods to come up with a lead drug compound. This article is an overview of the virtual methodologies, which assist in finding novel hits and help in the progression of drug development in a short period with a specific medicinal solution.

Molecular modelling of the thermophysical properties of fluids: expectations, limitations, gaps and opportunities

This manuscript provides an overview of the current state of the art in terms of the molecular modelling of the thermophysical properties of fluids. It is intended to manage the expectations and serve as guidance to practising physical chemists, chemical physicists and engineers in terms of the scope and accuracy of the more commonly available intermolecular potentials along with the peculiarities of the software and methods employed in molecular simulations while providing insights on the gaps and opportunities available in this field. The discussion is focused around case studies which showcase both the precision and the limitations of frequently used workflows.

Combining Group-Contribution Concept and Graph Neural Networks Toward Interpretable Molecular Property Models

Quantitative structure-property relationships (QSPRs) are important tools to facilitate and accelerate the discovery of compounds with desired properties. While many QSPRs have been developed, they are associated with various shortcomings such as a lack of generalizability and modest accuracy. Albeit various machine-learning and deep-learning techniques have been integrated into such models, another shortcoming has emerged in the form of a lack of transparency and interpretability of such models. In this work, two interpretable graph neural network (GNN) models (attentive group-contribution (AGC) and group-contribution-based graph attention (GroupGAT)) are developed by integrating fundamentals using the concept of group contributions (GC). The interpretability consists of highlighting the substructure with the highest attention weights in the latent representation of the molecules using the attention mechanism. The proposed models showcased better performance compared to classical group-contribution models, as well as against various other GNN models describing the aqueous solubility, melting point, and enthalpies of formation, combustion, and fusion of organic compounds. The insights provided are consistent with insights obtained from the semiempirical GC models confirming that the proposed framework allows highlighting the important substructures of the molecules for a specific property.

Identification of pyrimidinyl piperazines as non-iminosugar glucocerebrosidase (GCase) pharmacological chaperones

Glucocerebrosidase (GCase) is a lysosomal enzyme encoded by the GBA1 gene, loss of function variants of which cause an autosomal recessive lysosomal storage disorder, Gaucher disease (GD). Heterozygous variants of GBA1 are also known as the strongest common genetic risk factor for Parkinson's disease (PD). Restoration of GCase enzymatic function using a pharmacological chaperone strategy is considered a promising therapeutic approach for PD and GD. We identified compound 4 as a GCase pharmacological chaperone with sub-micromolar activity from a high-throughput screening (HTS) campaign. Compound 4 was further optimised to ER-001230194 (compound 25). ER-001230194 shows improved ADME and physicochemical properties and therefore represents a novel pharmacological chaperone with which to investigate GCase pharmacology further.

Computer-Aided Drug Design towards New Psychotropic and Neurological Drugs

Central nervous system (CNS) disorders are a therapeutic area in drug discovery where demand for new treatments greatly exceeds approved treatment options. This is complicated by the high failure rate in late-stage clinical trials, resulting in exorbitant costs associated with bringing new CNS drugs to market. Computer-aided drug design (CADD) techniques minimise the time and cost burdens associated with drug research and development by ensuring an advantageous starting point for pre-clinical and clinical assessments. The key elements of CADD are divided into ligand-based and structure-based methods. Ligand-based methods encompass techniques including pharmacophore modelling and quantitative structure activity relationships (QSARs), which use the relationship between biological activity and chemical structure to ascertain suitable lead molecules. In contrast, structure-based methods use information about the binding site architecture from an established protein structure to select suitable molecules for further investigation. In recent years, deep learning techniques have been applied in drug design and present an exciting addition to CADD workflows. Despite the difficulties associated with CNS drug discovery, advances towards new pharmaceutical treatments continue to be made, and CADD has supported these findings. This review explores various CADD techniques and discusses applications in CNS drug discovery from 2018 to November 2022.

Regression analysis for predicting the elasticity of liquid crystal elastomers

It is highly desirable but difficult to understand how microscopic molecular details influence the macroscopic material properties, especially for soft materials with complex molecular architectures. In this study we focus on liquid crystal elastomers (LCEs) and aim at identifying the design variables of their molecular architectures that govern their macroscopic deformations. We apply the regression analysis using machine learning (ML) to a database containing the results of coarse grained molecular dynamics simulations of LCEs with various molecular architectures. The predictive performance of a surrogate model generated by the regression analysis is also tested. The database contains design variables for LCE molecular architectures, system and simulation conditions, and stress-strain curves for each LCE molecular system. Regression analysis is applied using the stress-strain curves as objective variables and the other factors as explanatory variables. The results reveal several descriptors governing the stress-strain curves. To test the predictive performance of the surrogate model, stress-strain curves are predicted for LCE molecular architectures that were not used in the ML scheme. The predicted curves capture the characteristics of the results obtained from molecular dynamics simulations. Therefore, the ML scheme has great potential to accelerate LCE material exploration by detecting the key design variables in the molecular architecture and predicting the LCE deformations.

Fast Prediction of the Equivalent Alkane Carbon Number Using Graph Machines and Neural Networks

The hydrophobicity of oils is a key parameter to design surfactant/oil/water (SOW) macro-, micro-, or nano-dispersed systems with the desired features. This essential physicochemical characteristic is quantitatively expressed by the equivalent alkane carbon number (EACN) whose experimental determination is tedious since it requires knowledge of the phase behavior of the SOW systems at different temperatures and for different surfactant concentrations. In this work, two mathematical models are proposed for the rapid prediction of the EACN of oils. They have been designed using artificial intelligence (machine-learning) methods, namely, neural networks (NN) and graph machines (GM). While the GM model is implemented from the SMILES codes of a 111-molecule training set of known EACN values, the NN model is fed with some σ-moment descriptors computed with the COSMOtherm software for the 111-molecule set. In a preliminary step, the leave-one-out algorithm is used to select, given the available data, the appropriate complexity of the two models. A comparison of the EACNs of liquids of a fresh set of 10 complex cosmetic and perfumery molecules shows that the two approaches provide comparable results in terms of accuracy and reliability. Finally, the NN and GM models are applied to nine series of homologous compounds, for which the GM model results are in better agreement with the experimental EACN trends than the NN model predictions. The results obtained by the GMs and by the NN based on σ-moments can be duplicated with the demonstration tool available for download as detailed in the Supporting Information.

Evaluating structure-based activity in a high-throughput assay for steroid biosynthesis

Data from a high-throughput human adrenocortical carcinoma assay (HT-H295R) for steroid hormone biosynthesis are available for >2000 chemicals in single concentration and 654 chemicals in multi-concentration (mc). Previously, a metric describing the effect size of a chemical on the biosynthesis of 11 hormones was derived using mc data referred to as the maximum mean Mahalanobis distance (maxmMd). However, mc HT-H295R assay data remain unavailable for many chemicals. This work leverages existing HT-H295R assay data by constructing structure-activity relationships to make predictions for data-poor chemicals, including: (1) identification of individual structural descriptors, known as ToxPrint chemotypes, associated with increased odds of affecting estrogen or androgen synthesis; (2) a random forest (RF) classifier using physicochemical property descriptors to predict HT-H295R maxmMd binary (positive or negative) outcomes; and, (3) a local approach to predict maxmMd binary outcomes using nearest neighbors (NNs) based on two types of chemical fingerprints (chemotype or Morgan). Individual chemotypes demonstrated high specificity (85-98%) for modulators of estrogen and androgen synthesis but with low sensitivity. The best RF model for maxmMd classification included 13 predicted physicochemical descriptors, yielding a balanced accuracy (BA) of 71% with only modest improvement when hundreds of structural features were added. The best two NN models for binary maxmMd prediction demonstrated BAs of 85 and 81% using chemotype and Morgan fingerprints, respectively. Using an external test set of 6302 chemicals (lacking HT-H295R data), 1241 were identified as putative estrogen and androgen modulators. Combined results across the three classification models (global RF model and two local NN models) predict that 1033 of the 6302 chemicals would be more likely to affect HT-H295R bioactivity. Together, these in silico approaches can efficiently prioritize thousands of untested chemicals for screening to further evaluate their effects on steroid biosynthesis.

Random forest algorithm-based classification model of pesticide aquatic toxicity to fishes

Aquatic toxicity of pesticides can result in poisoning of many non-target organisms, of which various fishes are the most prominent one. It is a challenge to predict the toxicity (LC₅₀) classes of organic pesticides to various fish species from global QSAR models with a larger applicability domain. In this paper, by applying the random forest (RF) algorithm for a two-class problem, only eight molecular descriptors were used to develop a quantitative structure-activity relationship (QSAR) model for 1106 toxicity data (96 h, LC₅₀) of organic pesticides to various fish species including Oncorhynchus mykiss, Lepomis macrochirus, Pimephales promelas, Brachydanio rerio, Cyprinodon, Cyprinus carpio, etc. By the prediction of the optimal RF Model I (ntree =280, mtry = 3 and nodesize = 5), the training set (885 organic pesticides) has the prediction accuracies of 99.6% for Class 1 (LC₅₀ ≤ 10) and 96.7% for Class 2 (LC₅₀ > 10); the test set (221 organic pesticides) has the accuracies being 90.8% for Class 1 and 91.2% for Class 2. The optimal RF Model I is satisfactory compared with other QSAR model reported in the literature, although its descriptor subset is small.

Cleaning and microstructural effects of amyl acetate on pulp chamber dentin impregnated with epoxy resin-based endodontic sealer

OBJECTIVE

To evaluate the cleaning potential of 95% ethanol, acetone, and amyl acetate solutions used solely or in association, to remove epoxy resin-based sealer residues from pulp chamber dentin and their microstructural effects.

MATERIALS AND METHODS

One hundred and eighty bovine incisor specimens were divided into nine groups according to the cleaning protocol: ET (ethanol); AC (acetone); AA (amyl acetate); E1: AA+AC; E2: AA+ET; E3: AC+ET; E4: AA+AC+ET; PC (positive control), and NC (negative control). All groups were impregnated with epoxy resin-sealer, except NC. Ninety specimens were divided into groups (n = 10) for evaluation of persistence of residues and amount of open dentinal tubules by SEM analysis and evaluation of chemical compounds on the dentin surface after cleaning with electron dispersive spectroscopy. The others 90 specimens were submitted to Knoop microhardness evaluation. Persistence of residues data were submitted to the Kruskal Wallis and Dunn tests (α = 0.05). Open dentinal tubules and microhardness data were submitted to one-way ANOVA and Mann Whitney tests (α = 0.05).

RESULTS

AA and E4 protocols showed the lowest persistence of residues. E4 group had the highest incidence of open dentinal tubules. E3 and E4 groups showed no changes in the atomic ratio Ca/P, which was similar to NC group. E4 group did not present W, an element presents in all the other groups. ET and E4 protocols showed the smallest reduction in dentin microhardness.

CONCLUSIONS

The combination of amyl acetate, acetone and ethanol is the most effective and safe protocol to remove epoxy sealer residues on pulp chamber dentin. Moreover, it has the lowest microhardness reduction.

CLINICAL SIGNIFICANCE

The combined use of amyl acetate, acetone, and ethanol enhanced the cleaning of pulp chamber dentin with minimal microstructural damage.

LGB-Stack: Stacked Generalization with LightGBM for Highly Accurate Predictions of Polymer Bandgap

Recently, the Ramprasad group reported a quantitative structure-property relationship (QSPR) model for predicting the E _gap values of 4209 polymers, which yielded a test set R ² score of 0.90 and a test set root-mean-square error (RMSE) score of 0.44 at a train/test split ratio of 80/20. In this paper, we present a new QSPR model named LGB-Stack, which performs a two-level stacked generalization using the light gradient boosting machine. At level 1, multiple weak models are trained, and at level 2, they are combined into a strong final model. Four molecular fingerprints were generated from the simplified molecular input line entry system notations of the polymers. They were trimmed using recursive feature elimination and used as the initial input features for training the weak models. The output predictions of the weak models were used as the new input features for training the final model, which completes the LGB-Stack model training process. Our results show that the best test set R ² and the RMSE scores of LGB-Stack at the train/test split ratio of 80/20 were 0.92 and 0.41, respectively. The accuracy scores further improved to 0.94 and 0.34, respectively, when the train/test split ratio of 95/5 was used.

General Equation to Express Changes in the Physicochemical Properties of Organic Homologues

Changes in various physicochemical properties (P _(n)) of organic compounds with the number of carbon atoms (n) can be roughly divided into linear and nonlinear changes. To date, there has been no general equation to express nonlinear changes in the properties of organic homologues. This study proposes a general equation expressing nonlinear changes in the physicochemical properties of organic homologues, including boiling point, viscosity, ionization potential, and vapor pressure, named the "NPOH equation", as follows: P _(n) = P ₍₁₎ α ^n - 1 e ^{∑_i=2 n(β/(i - 1))} where α and β are adjustable parameters, and P ₍₁₎ represents the property of the starting compound (pseudo-value at n = 1) of each homologue. The results show that various nonlinear changes in the properties of homologues can be expressed by the NPOH equation. Linear and nonlinear changes in the properties of homologues can all be correlated with n and the "sum of carbon number effects", ∑_i=2 ⁿ(1/i - 1). Using these two parameters, a quantitative correlation equation can be established between any two properties of each homologue, providing convenient mutual estimation of the properties of a homologue series. The NPOH equation can also be used in property correlation for structures with functionality located elsewhere along a linear alkyl chain as well as for branched organic compounds. This work can provide new perspectives for studying quantitative structure-property relationships.

Machine Learning for Electrocatalyst and Photocatalyst Design and Discovery

Electrocatalysts and photocatalysts are key to a sustainable future, generating clean fuels, reducing the impact of global warming, and providing solutions to environmental pollution. Improved processes for catalyst design and a better understanding of electro/photocatalytic processes are essential for improving catalyst effectiveness. Recent advances in data science and artificial intelligence have great potential to accelerate electrocatalysis and photocatalysis research, particularly the rapid exploration of large materials chemistry spaces through machine learning. Here a comprehensive introduction to, and critical review of, machine learning techniques used in electrocatalysis and photocatalysis research are provided. Sources of electro/photocatalyst data and current approaches to representing these materials by mathematical features are described, the most commonly used machine learning methods summarized, and the quality and utility of electro/photocatalyst models evaluated. Illustrations of how machine learning models are applied to novel electro/photocatalyst discovery and used to elucidate electrocatalytic or photocatalytic reaction mechanisms are provided. The review offers a guide for materials scientists on the selection of machine learning methods for electrocatalysis and photocatalysis research. The application of machine learning to catalysis science represents a paradigm shift in the way advanced, next-generation catalysts will be designed and synthesized.

Improvement of theoretical UV-Vis spectra calculations by empirical solvatochromic parameters: Case study of 5-arylazo-3-cyano-1-ethyl-6-hydroxy-4-methyl-2-pyridones

In order to improve the performance of theoretical UV-Vis spectra predictions, a theoretical and experimental study of solvatochromic properties of ten azo pyridone dyes has been performed. For quantitative estimation of intermolecular solvent-solute interactions, a concept of the linear solvation energy relationships has been applied using Kamlet-Taft and Catalán models. Theoretical UV-Vis spectra for all dyes have been calculated using four TD-DFT methods in nine different solvents with the aim to define the most reliable model. Finally, new polylinear equations for more accurate theoretical prediction of UV-Vis maxima are developed using empirical Kamlet-Taft and Catalán solvent parameters as additive corrections for specific and nonspecific solvent-solute interactions.

Development and Evaluation of Geometry Optimization Algorithms in Conjunction with ANI Potentials

An efficient yet accurate method for producing a large amount of energy data for molecular mechanical force field (MMFF) parameterization is on demand, especially for torsional angle parameters which are typically derived to reproduce ab initio rotational profiles or torsional potential energy surfaces (PESs). Recently, an active learning potential (ANI-1x) for organic molecules which can produce smooth and physically meaningful PESs has been developed. The high efficiency and accuracy make ANI-1x especially attractive for geometry optimization at low cost. To apply the ANI-1x potential in MMFF parameterization, one needs to perform constrained geometry optimization. In this work, we first developed a computational protocol to constrain rotatable torsional angles and other geometric parameters for a molecule whose geometry is described by Cartesian coordinates. The constraint is successfully achieved by force projection for the two conjugated gradient (CG) algorithms. We then conducted large-scale assessments on ANI-1x along with four different optimization algorithms in reproducing DFT energies and geometries for two CG algorithms, CG backtracking line search (CG-BS) and CG Wolfe line search (CG-WS), and two quasi-Newton algorithms, Broyden-Fletcher-Goldfarb-Shanno (BFGS) and low-memory BFGS (L-BFGS). Note that CG-BS is a new algorithm we developed in this work. All four algorithms take the ANI energies and forces to optimize a molecule geometry. Last, we conducted a large-scale assessment of applying ANI-1x in MMFF development in three aspects. First, we performed full optimizations for 100 drug molecules, each consisting of five distinct conformations. The average root-mean-square error (RMSE) between ANI-1x and DFT is about 1.3 kcal/mol, and the root-mean-square displacement (RMSD) of heavy atoms is about 0.35 Å. Second, we generated torsional PESs for 160 organic molecules, and constrained optimizations were performed for up to 18 conformations for each PES. We found that the RMSE of all the conformers is 1.23 kcal/mol. Last, we carried out constrained optimizations for alanine dipeptide with both ϕ and φ angles being frozen. The Ramachandran plots indicate that the two CG algorithms in conjunction with the ANI-1x potential could well reproduce the DFT-optimized geometries and torsional PESs. We concluded that CG-BS and CG-WS are good choices for generating PESs, while CG-WS or BFGS is ideal for performing full geometry optimization. With the continuously increased quality of ANI, it is expected that the computational algorithms and protocols presented in this work will have great applications in improving the quality of an existing small-molecule MMFF.

Pharmacokinetics and tissue distribution of Trans-ferulic acid-4-β-glucoside in rats by UPLC-MS/MS

Trans-ferulic acid-4-β-glucoside (FAG) is a monomer extracted from Radix Aconiti Lateralis Preparata, which is a potential candidate for the prevention and treatment of the cold injury. To determine the concentration FAG in rats, it is essential to develop an ultra-performance liquid chromatography coupled with tandem mass spectrometry (UPLC-MS/MS) method. The chromatographic separation was achieved by ACQUITY UPLC BEH C18 column(2.1×50mm, 1.7μm). A Xevo triple quadrupole tandem mass spectrometer was used to quantitative determination of FAG in the negative ion mode. The standard calibration curve was linear over the concentration range of 0.1-100 μg/mL and 0.0626-31.28 μg/g for rat plasma and liver tissue homogenates samples, separately. The inter-and intra-batch precision (RSD%) of the assay was ≤ 8.29% and accuracy (RE%) ranged from -7.41 to 10.99%. The matrix effect was between 92.99 and 102.39%. The oral absolute bioavailability of FAG was obtained as 1.80%. The results of tissue distribution suggested that FAG spread rarely in liver and brown adipose, which was not propitious to exert its ability to treat cold injury. In general, the above studies were significant to provide necessary information for further study.

An open-source framework for fast-yet-accurate calculation of quantum mechanical features

We present the open-source framework kallisto that enables the efficient and robust calculation of quantum mechanical features for atoms and molecules. For a benchmark set of 49 experimental molecular polarizabilities,...

Comparisons of Molecular Structure Generation Methods Based on Fragment Assemblies and Genetic Graphs

The use of quantitative structure-property relationships (QSPRs) helps in predicting molecular properties for several decades, while the automatic design of new molecular structures is still emerging. The choice of algorithms to generate molecules is not obvious and is related to several factors such as the desired chemical diversity (according to an initial dataset's content) and the level of construction (the use of atoms, fragments, pattern-based methods). In this paper, we address the problem of molecular structure generation by revisiting two approaches: fragment-based methods (FMs) and genetic-based methods (GMs). We define a set of indices to compare generation methods on a specific task. New indices inform about the explored data space (coverage), compare how the data space is explored (representativeness), and quantifies the ratio of molecules satisfying requirements (generation specificity) without the use of a database composed of real chemicals as a reference. These indices were employed to compare generations of molecules fulfilling the desired property criterion, evaluated by QSPR.

Predicting Critical Micelle Concentrations for Surfactants Using Graph Convolutional Neural Networks

Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally-tensiometry-is laborious and expensive. In this study, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. In particular, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy on a more inclusive data set than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants using a single model. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this behavior to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics.