A General Guidebook for the Theoretical Prediction of Physicochemical Properties of Chemicals for Regulatory Purposes

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.

Quantitative structure-property relationship modelling on autoignition temperature: evaluation and comparative analysis

The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodological practices for AIT predictions, this study conducts a benchmark investigation on Quantitative Structure-Property Relationship (QSPR) modelling for AIT. As novelties of this work, three significant advancements are implemented in the AIT modelling process, including explicit consideration of data quality, utilization of state-of-the-art feature engineering workflows, and the innovative application of graph-based deep learning techniques, which are employed for the first time in AIT prediction. Specifically, three traditional QSPR models (multi-linear regression, support vector regression, and artificial neural networks) are evaluated, alongside the assessment of a deep-learning model employing message passing neural network architecture supplemented by graph-data augmentation techniques.

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.

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.

QSPR models to predict the physical hazards of mixtures: a state of art

Physical hazards of chemical mixtures, associated for example with their fire or explosion risks, are generally characterized using experimental tools. These tests can be expensive, complex, long to perform and even dangerous for operators. Therefore, for several years and especially with the implementation of the REACH regulation, predictive methods like quantitative structure-property relationships have been encouraged as alternatives tests to determine (eco)toxicological but also physical hazards of chemical substances. Initially, these approaches were intended for pure products, by considering a molecular similarity principle. However, additional to those for pure products, QSPR models for mixtures recently appeared and represent an increasing field of research. This study proposes a state of the art of existing QSPR models specifically dedicated to the prediction of the physical hazards of mixtures. Identified models have been analysed on the key elements of model development (experimental data and fields of application, descriptors used, development and validation methods). It draws up an overview of the potential and limitations of current models as well as areas of progress towards enlarged deployment as a complement to experimental characterizations, for example in the search for safer substances (according to safety-by-design concepts).

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.

New Method of NPOH Equation-Based to Estimate the Physicochemical Properties of Noncyclic Alkanes

Changes in various physicochemical properties (P _(n)) of noncyclic alkanes can be roughly classified as linear and nonlinear changes. In our previous study, the NPOH equation was proposed to express nonlinear changes in the properties of organic homologues. Until now, there has been no general equation to express nonlinear changes in the properties of noncyclic alkanes involving linear and branched alkane isomers. This work, on the basis of NPOH equation, proposes a general equation to express nonlinear changes in the physicochemical properties of noncyclic alkanes, including a total of 12 properties, boiling point, critical temperature, critical pressure, acentric factor, heat capacity, liquid viscosity, and flash point, named as the "NPNA equation", as follows: ln(P _(n)) = a + b(n - 1) + c(S _CNE) + d (ΔAOEI) + f(ΔAIMPI), where a, b, c, and f are coefficients, and P _(n) represents the property of the alkane with n carbon atom number. n, S _CNE, ΔAOEI, and ΔAIMPI are number of carbon atoms, sum of carbon number effects, average odd-even index difference, and average inner molecular polarizability index difference, respectively. The obtained results show that various nonlinear changes in the properties of noncyclic alkanes can be expressed by the NPNA equation. Nonlinear and linear change properties of noncyclic alkanes can be correlated with four parameters, n, S _CNE, ΔAOEI, and ΔAIMPI. The NPNA equation has the advantages of uniform expression, usage of fewer parameters, and high estimation accuracy. Furthermore, using the above four parameters, a quantitative correlation equation can be established between any two properties of noncyclic alkanes. Employing the obtained equations as model equations, the property data of noncyclic alkanes, involving 142 critical temperatures, 142 critical pressures, 115 acentric factors, 116 flash points, 174 heat capacities, 142 critical volumes, and 155 gas enthalpies of formation, a total of 986 values, were predicted, which have not be experimentally measured. NPNA equation not only can provide a simple and convenient estimation or prediction method for the properties of noncyclic alkanes but also can provide new perspectives for studying quantitative structure-property relationships of branched organic compounds.

Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach

For many experimentally measured chemical properties that cannot be directly computed from first-principles, the existing physics-based models do not extrapolate well to out-of-sample molecules, and experimental datasets themselves are too small for traditional machine learning (ML) approaches. To overcome these limitations, we apply a transfer learning approach, whereby we simultaneously train a multi-target regression model on a small number of molecules with experimentally measured values and a large number of molecules with related computed properties. We demonstrate this methodology on predicting the experimentally measured impact sensitivity of energetic crystals, finding that both characteristics of the computed dataset and model architecture are important to prediction accuracy of the small experimental dataset. Our directed-message passing neural network (D-MPNN) ML model using transfer learning outperforms direct-ML and physics-based models on a diverse test set, and the new methods described here are widely applicable to modeling many other structure-property relationships.

Quantitative Structure-Toxicity Relationship in Bioactive Molecules from a Conceptual DFT Perspective

The preclinical drug discovery stage often requires a large amount of costly and time-consuming experiments using huge sets of chemical compounds. In the last few decades, this process has undergone significant improvements by the introduction of quantitative structure-activity relationship (QSAR) modelling that uses a certain percentage of experimental data to predict the biological activity/property of compounds with similar structural skeleton and/or containing a particular functional group(s). The use of machine learning tools along with it has made life even easier for pharmaceutical researchers. Here, we discuss the toxicity of certain sets of bioactive compounds towards Pimephales promelas and Tetrahymena pyriformis in terms of the global conceptual density functional theory (CDFT)-based descriptor, electrophilicity index (ω). We have compared the results with those obtained by using the commonly used hydrophobicity parameter, logP (where P is the n-octanol/water partition coefficient), considering the greater ease of computing the ω descriptor. The Human African trypanosomiasis (HAT) curing activity of 32 pyridyl benzamide derivatives is also studied against Tryphanosoma brucei. In this review article, we summarize these multiple linear regression (MLR)-based QSAR studies in terms of electrophilicity (ω, ω2) and hydrophobicity (logP, (logP)2) parameters.

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.

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.

Bio-based alternatives to volatile silicones: Relationships between chemical structure, physicochemical properties and functional performances

Emollient oils are ubiquitous ingredients of personal care products, especially skin care and hair care formulations. They offer excellent spreading properties and give end-use products a soft, pleasant and non-sticky after-feel. Emollients belong to various petro- or bio-based chemical families among which silicone oils, hydrocarbons and esters are the most prominent. Silicones have exceptional physicochemical and sensory properties but their high chemical stability results in very low biodegradability and a high bioaccumulation potential. Nowadays, consumers are increasingly responsive to environmental issues and demand more environmentally friendly products. This awareness strongly encourages cosmetics industries to develop bio-based alternatives to silicone oils. Finding effective silicon-free emollients requires understanding the molecular origin of emollience. This review details the relationships between the molecular structures of emollients and their physicochemical properties as well as the resulting functional performances in order to facilitate the design of alternative oils with suitable physicochemical and sensory properties. The molecular profile of an ideal emollient in terms of chemical function (alkane, ether, ester, carbonate, alcohol), optimal number of carbons and branching is established to obtain an odourless oil with good spreading on the skin. Since none of the carbon-based emollients alone can imitate the non-sticky and dry feel of silicone oils, it is judicious to blend alkanes and esters to significantly improve both the sensory properties and the solubilizing properties of the synergistic mixture towards polar ingredients (sun filters, antioxidants, fragrances). Finally, it is shown how modelling tools (QSPR, COSMO-RS and neural networks) can predict in silico the key properties of hundreds of virtual candidate molecules in order to synthesize only the most promising whose predicted properties are close to the specifications.

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.

Machine learning of consistent thermodynamic models using automatic differentiation

We propose a data-driven method to describe consistent equations of state (EOS) for arbitrary systems. Complex EOS are traditionally obtained by fitting suitable analytical expressions to thermophysical data. A key aspect of EOS is that the relationships between state variables are given by derivatives of the system free energy. In this work, we model the free energy with an artificial neural network and utilize automatic differentiation to directly learn the derivatives of the free energy. We demonstrate this approach on two different systems, the analytic van der Waals EOS and published data for the Lennard-Jones fluid, and we show that it is advantageous over direct learning of thermodynamic properties (i.e., not as derivatives of the free energy but as independent properties), in terms of both accuracy and the exact preservation of the Maxwell relations. Furthermore, the method implicitly provides the free energy of a system without explicit integration.

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.

Comparison and Validation of Force Fields for Deep Eutectic Solvents in Combination with Water and Alcohol Dehydrogenase

Deep eutectic solvents (DESs) have become popular as environmental-friendly solvents for biocatalysis. Molecular dynamics (MD) simulations offer an in-depth analysis of enzymes in DESs, but their performance depends on the force field chosen. Here, we present a comprehensive validation of three biomolecular force fields (CHARMM, Amber, and OPLS) for simulations of alcohol dehydrogenase (ADH) in DESs composed of choline chloride and glycerol/ethylene glycol with varying water contents. Different properties (e.g., protein structure and flexibility, solvation layer, and H-bonds) were used for validation. For two properties (viscosity and water activity) also experiments were performed. The viscosity was calculated with the periodic perturbation method, whereby its parameter dependency is disclosed. A modification of Amber was identified as the best-performing model for low water contents, whereas CHARMM outperforms the other models at larger water concentrations. An analysis of ADH's structure and interactions with the DESs revealed similar predictions for Amber and CHARMM.

An easy-to-use and general nuclear magnetic resonance method for the determination of partition coefficients of drugs and natural products

The lipophilicity of a drug is an important parameter for its eventual development by the pharmaceutical industry. It is usually measured by HPLC following partition of the compound between water and 1-octanol. We present here an alternative, simple, sensitive and quantitative ¹ H nuclear magnetic resonance (NMR) method for the experimental measurement of partition coefficients of natural compounds and pharmaceutical drugs. It is based on measuring concentrations in the water phase, before and after partitioning and equilibration between water and octanol, using the ERETIC (Electronic Reference To Access In Vivo Concentration) technique. The signal to noise ratio is improved by a Water Suppression by Excitation Sculpting sequence. Quantification is based on an electronic reference signal and does not need addition of a reference compound. The log P values of 22 natural metabolites and four pharmaceutical drugs were determined and the experimental results are in excellent agreement with literature data. The experiments were run on ~2 mg material. This technique proved to be robust, reproducible and suitable for log P values between -2 and +2.

Energy-entropy prediction of octanol-water logP of SAMPL7 N-acyl sulfonamide bioisosters

Partition coefficients quantify a molecule's distribution between two immiscible liquid phases. While there are many methods to compute them, there is not yet a method based on the free energy of each system in terms of energy and entropy, where entropy depends on the probability distribution of all quantum states of the system. Here we test a method in this class called Energy Entropy Multiscale Cell Correlation (EE-MCC) for the calculation of octanol-water logP values for 22 N-acyl sulfonamides in the SAMPL7 Physical Properties Challenge (Statistical Assessment of the Modelling of Proteins and Ligands). EE-MCC logP values have a mean error of 1.8 logP units versus experiment and a standard error of the mean of 1.0 logP units for three separate calculations. These errors are primarily due to getting sufficiently converged energies to give accurate differences of large numbers, particularly for the large-molecule solvent octanol. However, this is also an issue for entropy, and approximations in the force field and MCC theory also contribute to the error. Unique to MCC is that it explains the entropy contributions over all the degrees of freedom of all molecules in the system. A gain in orientational entropy of water is the main favourable entropic contribution, supported by small gains in solute vibrational and orientational entropy but offset by unfavourable changes in the orientational entropy of octanol, the vibrational entropy of both solvents, and the positional and conformational entropy of the solute.

Quantitative Kinetic Model of CO2 Absorption in Aqueous Tertiary Amine Solvents

Aqueous tertiary amine solutions are increasingly used in industrial CO₂ capture operations because they are more energy-efficient than primary or secondary amines and demonstrate higher CO₂ absorption capacity. Yet, tertiary amine solutions have a significant drawback in that they tend to have lower CO₂ absorption rates. To identify tertiary amines that absorb CO₂ faster, it would be efficacious to have a quantitative and predictive model of the rate-controlling processes. Despite numerous attempts to date, this goal has been elusive. The present computational approach achieves this goal by focusing on the reaction of CO₂ with OH^- forming HCO₃^-. The performance of the resulting model is demonstrated for a consistent experimental data set of the absorption rates of CO₂ for 24 different aqueous tertiary amine solvents. The key to the new model's success is the manner in which the free energy barrier for the reaction of CO₂ with OH^- is evaluated from the differences among the solvation free energies of CO₂, OH^-, and HCO₃^-, while the pK_a of the amines controls the concentration of OH^-. These solvation energies are obtained from molecular dynamics simulations. The experimental value of the free energy of reaction of CO₂ with pure water is combined with information about measured rates of absorption of CO₂ in an aqueous amine solvent in order to calibrate the absorption rate model. This model achieves a relative accuracy better than 0.1 kJ mol^-1 for the free energies of activation for CO₂ absorption in aqueous amine solutions and 0.07 g L^-1 min^-1 for the absorption rate of CO₂. Such high accuracies are necessary to predict the correct experimental ranking of CO₂ absorption rates, thus providing a quantitative approach of practical interest.

Viscosity models for ionic liquids and their mixtures

Review of principles and limitations of viscosity models for ionic liquids and their mixtures focusing on the use of inappropriate mixing rules for molten salts.

Chemoinformatics for the Safety of Energetic and Reactive Materials at Ineris

The characterization of physical hazards of substances is a key information to manage the risks associated to their use, storage and transport. With decades of work in this area, Ineris develops and implements cutting-edge experimental facilities allowing such characterizations at different scales and under various conditions to study all of the dreaded accident scenarios. This review presents the efforts engaged by Ineris more recently in the field of chemoinformatics to develop and use new predictive methods for the anticipation and management of industrials risks associated to energetic and reactive materials as a complement to experiments. An overview of the methods used for the development of Quantitative Structure-Property Relationships for physical hazards are presented and discussed regarding the specificities associated to this class of properties. A review of models developed at Ineris is also provided from the first tentative models on the explosivity of nitro compounds to the successful application to the flammability of organic mixtures. Then, a discussion is proposed on the use of QSPR models. Good practices for robust use for QSPR models are recalled with specific comments related to physical hazards, notably for regulatory purpose. Dissemination and training efforts engaged by Ineris are also presented. The potential offered by these predictive methods in terms of in silico design and for the development of new intrinsically safer technologies in safety-by-design strategies is finally discussed. At last, challenges and perspectives to extend the application of chemoinformatics in the field of safety and in particular for the physical hazards of energetic and reactive substances are proposed.

Solvent similarity index

The Solvent Similarity Index (SSI) is a quantitative parameter we introduce for the comparison of the solvation properties of any solvent or solvent mixture. The Surface Site Interaction Model for Liquids at Equilibrium (SSIMPLE) was used to calculate the free energy of solvation of a single Surface Site Interaction Point (SSIP) on a solute. The SSIP representation of molecular surfaces was used to calculate the free energy of solvation for all possible solute polarities, generating a unique solvation profile for any solvent or solvent mixture. Quantitative comparison of the solvation profiles of two solvents was used as the basis for calculating the solvation similarity index. Values of SSI were calculated for all pairwise comparisons of 261 pure solvents at 298 K, and the results were used to classify solvents into groups according to their solvation properties. Applications to understanding the solvation properties of binary solvent mixtures and for identification of alternative solvents are illustrated.

A New Machine-Learning Tool for Fast Estimation of Liquid Viscosity. Application to Cosmetic Oils

The viscosities of pure liquids are estimated at 25 °C, from their molecular structures, using three modeling approaches: group contributions, COSMO-RS σ-moment-based neural networks, and graph machines. The last two are machine-learning methods, whereby models are designed and trained from a database of viscosities of 300 molecules at 25 °C. Group contributions and graph machines make use of the 2D-structures only (the SMILES codes of the molecules), while neural networks estimations are based on a set of five descriptors: COSMO-RS σ-moments. For the first time, leave-one-out is used for graph machine selection, and it is shown that it can be replaced with the much faster virtual leave-one-out algorithm. The database covers a wide diversity of chemical structures, namely, alkanes, ethers, esters, ketones, carbonates, acids, alcohols, silanes, and siloxanes, as well as different chemical backbone, i.e., straight, branched, or cyclic chains. A comparison of the viscosities of liquids of an independent set of 22 cosmetic oils shows that the graph machine approach provides the most accurate results given the available data. The results obtained by the neural network based on sigma-moments and by the graph machines can be duplicated easily by using a demonstration tool based on the Docker technology, available for download as explained in the Supporting Information. This demonstration also allows the reader to predict, at 25 °C, the viscosity of any liquid of moderate molecular size (M < 600 Da) that contains C, H, O, or Si atoms, starting either from its SMILES code or from its σ-moments computed with the COSMOtherm software.

Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge

The SAMPL Challenges aim to focus the biomolecular and physical modeling community on issues that limit the accuracy of predictive modeling of protein-ligand binding for rational drug design. In the SAMPL5 log D Challenge, designed to benchmark the accuracy of methods for predicting drug-like small molecule transfer free energies from aqueous to nonpolar phases, participants found it difficult to make accurate predictions due to the complexity of protonation state issues. In the SAMPL6 log P Challenge, we asked participants to make blind predictions of the octanol-water partition coefficients of neutral species of 11 compounds and assessed how well these methods performed absent the complication of protonation state effects. This challenge builds on the SAMPL6 p[Formula: see text] Challenge, which asked participants to predict p[Formula: see text] values of a superset of the compounds considered in this log P challenge. Blind prediction sets of 91 prediction methods were collected from 27 research groups, spanning a variety of quantum mechanics (QM) or molecular mechanics (MM)-based physical methods, knowledge-based empirical methods, and mixed approaches. There was a 50% increase in the number of participating groups and a 20% increase in the number of submissions compared to the SAMPL5 log D Challenge. Overall, the accuracy of octanol-water log P predictions in SAMPL6 Challenge was higher than cyclohexane-water log D predictions in SAMPL5, likely because modeling only the neutral species was necessary for log P and several categories of method benefited from the vast amounts of experimental octanol-water log P data. There were many highly accurate methods: 10 diverse methods achieved RMSE less than 0.5 log P units. These included QM-based methods, empirical methods, and mixed methods with physical modeling supported with empirical corrections. A comparison of physical modeling methods showed that QM-based methods outperformed MM-based methods. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92 ± 0.13, 0.48 ± 0.06, 0.47 ± 0.05, and 0.50 ± 0.06, respectively.

Assessing Graph-based Deep Learning Models for Predicting Flash Point

Flash points of organic molecules play an important role in preventing flammability hazards and large databases of measured values exist, although millions of compounds remain unmeasured. To rapidly extend existing data to new compounds many researchers have used quantitative structure-property relationship (QSPR) analysis to effectively predict flash points. In recent years graph-based deep learning (GBDL) has emerged as a powerful alternative method to traditional QSPR. In this paper, GBDL models were implemented in predicting flash point for the first time. We assessed the performance of two GBDL models, message-passing neural network (MPNN) and graph convolutional neural network (GCNN), by comparing against 12 previous QSPR studies using more traditional methods. Our result shows that MPNN both outperforms GCNN and yields slightly worse but comparable performance with previous QSPR studies. The average R 2 and Mean Absolute Error (MAE) scores of MPNN are, respectively, 2.3 % lower and 2.0 K higher than previous comparable studies. To further explore GBDL models, we collected the largest flash point dataset to date, which contains 10575 unique molecules. The optimized MPNN gives a test data R 2 of 0.803 and MAE of 17.8 K on the complete dataset. We also extracted 5 datasets from our integrated dataset based on molecular types (acids, organometallics, organogermaniums, organosilicons, and organotins) and explore the quality of the model in these classes.

Prediction of Lipophilicity of some Quinolone Derivatives by using Quantitative Structure-Activity Relationship

OBJECTIVES

Quantitative structure activity relationship (QSAR) was used to study the partition coefficient of some quinolones and their derivatives.

METHODS

These molecules are broad-spectrum antibiotic pharmaceutics. First, data were divided into two categories of train and test (validation) sets using a random selection method. Second, three approaches, including stepwise selection (STS) (forward), genetic algorithm (GA), and simulated annealing (SA) were used to select the descriptors, to examine the effect feature selection methods. To find the relation between descriptors and partition coefficient, multiple linear regression (MLR), principal component regression (PCR) and partial least squares (PLS) were used.

RESULTS

QSAR study showed that both regression and descriptor selection methods have a vital role in the results. Different statistical metrics showed that the MLR-SA approach with (r2=0.96, q2=0.91, pred_r2=0.95) gives the best outcome.

CONCLUSION

The proposed expression by the MLR-SA approach can be used in the better design of novel quinolones and their derivatives.

Inverse-QSPR for de novo Design: A Review

The use of computer tools to solve chemistry-related problems has given rise to a large and increasing number of publications these last decades. This new field of science is now well recognized and labelled Chemoinformatics. Among all chemoinformatics techniques, the use of statistical based approaches for property predictions has been the subject of numerous research reflecting both new developments and many cases of applications. The so obtained predictive models relating a property to molecular features - descriptors - are gathered under the acronym QSPR, for Quantitative Structure Property Relationships. Apart from the obvious use of such models to predict property values for new compounds, their use to virtually synthesize new molecules - de novo design - is currently a high-interest subject. Inverse-QSPR (i-QSPR) methods have hence been developed to accelerate the discovery of new materials that meet a set of specifications. In the proposed manuscript, we review existing i-QSPR methodologies published in the open literature in a way to highlight developments, applications, improvements and limitations of each.

A Structure-Based Drug Discovery Paradigm

Structure-based drug design is becoming an essential tool for faster and more cost-efficient lead discovery relative to the traditional method. Genomic, proteomic, and structural studies have provided hundreds of new targets and opportunities for future drug discovery. This situation poses a major problem: the necessity to handle the “big data” generated by combinatorial chemistry. Artificial intelligence (AI) and deep learning play a pivotal role in the analysis and systemization of larger data sets by statistical machine learning methods. Advanced AI-based sophisticated machine learning tools have a significant impact on the drug discovery process including medicinal chemistry. In this review, we focus on the currently available methods and algorithms for structure-based drug design including virtual screening and de novo drug design, with a special emphasis on AI- and deep-learning-based methods used for drug discovery.

A Way towards Reliable Predictive Methods for the Prediction of Physicochemical Properties of Chemicals Using the Group Contribution and other Methods

Physicochemical properties of chemicals as referred to in this review include, for example, thermodynamic properties such as heat of formation, boiling point, toxicity of molecules and the fate of molecules whenever undergoing or accelerating (catalytic) a chemical reaction and therewith about chemical equilibrium, that is, the equilibrium in chemical reactions. All such properties have been predicted in literature by a variety of methods. However, for the experimental scientist for whom such predictions are of relevance, the accuracies are often far from sufficient for reliable application We discuss current practices and suggest how one could arrive at better, that is sufficiently accurate and reliable, predictive methods. Some recently published examples have shown this to be possible in practical cases. In summary, this review focuses on methodologies to obtain the required accuracies for the chemical practitioner and process technologist designing chemical processes. Finally, something almost never explicitly mentioned is the fact that whereas for some practical cases very accurate predictions are required, for other cases a qualitatively correct picture with relatively low correlation coefficients can be sufficient as a valuable predictive tool. Requirements for acceptable predictive methods can therefore be significantly different depending on the actual application, which are illustrated using real-life examples, primarily with industrial relevance. Furthermore, for specific properties such as the octanol-water partition coefficient more close collaboration between research groups using different methods would greatly facilitate progress in the field of predictive modelling.

Prediction of Lower Flammability Limits for Binary Hydrocarbon Gases by Quantitative Structure-A Property Relationship Approach

The lower flammability limit (LFL) is one of the most important parameters for evaluating the fire and explosion hazards of flammable gases or vapors. This study proposed quantitative structure−property relationship (QSPR) models to predict the LFL of binary hydrocarbon gases from their molecular structures. Twelve different mixing rules were employed to derive mixture descriptors for describing the structures characteristics of a series of 181 binary hydrocarbon mixtures. Genetic algorithm (GA)-based multiple linear regression (MLR) was used to select the most statistically effective mixture descriptors on the LFL of binary hydrocarbon gases. A total of 12 multilinear models were obtained based on the different mathematical formulas. The best model, issued from the norm of the molar contribution formula, was achieved as a six-parameter model. The best model was then rigorously validated using multiple strategies and further extensively compared to the previously published model. The results demonstrated the robustness, validity, and satisfactory predictivity of the proposed model. The applicability domain (AD) of the model was defined as well. The proposed best model would be expected to present an alternative to predict the LFL values of existing or new binary hydrocarbon gases, and provide some guidance for prioritizing the design of safer blended gases with desired properties.