Quantitative Structure-property Relationship Models Research Articles

In silico prediction of boiling point, octanol-water partition coefficient, and retention time index of polycyclic aromatic hydrocarbons through machine learning.

Polycyclic aromatic hydrocarbons (PAHs), a special class of persistent organic pollutants (POPs) with two or more aromatic rings, have received extensive attention owing to their carcinogenic, mutagenic, and teratogenic effects. Quantitative structure-property relationship (QSPR) is powerful chemometric method to correlate structural descriptors of PAHs with their physicochemical properties. In this manuscript, a QSPR study of PAHs was performed to predict their boiling point (bp), octanol-water partition coefficient (LogKow ), and retention time index (RI). In addition to traditional molecular descriptors, structural fingerprints play an important role in the correlation of the above properties. Three regression methods, partial least squares (PLS), multiple linear regression (MLR), and genetic function approximation (GFA), were used to establish QSPR models for each property of PAHs. The correlation coefficient (R2 test ) and root mean square error (RMSE) of best model were 0.980 and 24.39% (PLS), 0.979 and 35.80% (GFA), 0.926 and 22.90% (MLR) for bp, LogKow, and RI, respectively. The model proposed here can be used to estimate physicochemical properties and inform toxicity prediction of environmental chemicals.

Predicting toxicity of endocrine disruptors and blood–brain barrier permeability using chirality-sensitive descriptors and machine learning

Estrogen receptor (ER) mediated endocrine disruption and blood–brain barrier (BBB) permeability are two crucial pharmacological endpoints that must be assessed for any drug candidate. However, experimental testing is expensive and time-consuming, and in recent years, Quantitative Structure-Property Relationships (QSPRs) have emerged as a viable in silico alternative. However, most QSPR models reported on ER toxicity and BBB permeability have been carried out using 2D descriptors, whereas it has been established that ER binding and BBB permeability are stereoselective processes in which the spatial arrangement of atoms in the molecule plays a key role. The current study addresses this problem using a chirality-sensitive 3D-QSPR methodology entitled ‘EigenValue ANalysiS (EVANS). The EVANS approach merges information from 3D molecular structure with 2D physicochemical properties to generate eigenvalues which are used as descriptors in QSPR modelling. For chiral compounds, EVANS computes descriptors by considering distance attributes from a plethora of enantiomeric states, thereby accounting for the contributions of multiple conformers towards a particular biological endpoint. We deploy the EVANS methodology with machine learning algorithms to build predictive QSPR models for estrogen receptor (ER) mediated endocrine disruption and BBB permeability. Regression analyses of ER binding on a dataset of 132 chemical entities returned a robust and predictive model, with the support vector machine model having rtrain2=0.84 and rtest2=0.70. Classification models for BBB permeability on a dataset of 607 chemicals also showed high prediction accuracy, with the artificial neural network model showing the best performance (Accuracy = 0.85, AUC = 0.82, precision = 0.85, F1 score = 0.89). For comparison, conventional 2D-QSPR models were also built for these endpoints, and it was observed that EVANS generates eigenvalues that are superior to descriptors used in standard 2D-QSPR. Overall, our results demonstrate that EVANS is a powerful 3D-QSPR methodology that offers several advantages over existing QSAR/QSPR methods, and can be a useful computational tool in the pharmacological and toxicological evaluation of new and existing drugs.

Versatile in silico modelling of microplastics adsorption capacity in aqueous environment based on molecular descriptor and machine learning

To comprehensively evaluate the hazards of microplastics and their coexisting organic pollutants, the sorption capacity of microplastics is a major issue that is quantified through the microplastic-aqueous sorption coefficient (Kd). Almost all quantitative structure-property relationship (QSPR) models that describe Kd apply only to narrow, relatively homogeneous groups of reactants. Herein, non-hybrid QSPR-based models were developed to predict PE-water (KPE-w), PE-seawater (KPE-sw), PVC-water (KPVC-w) and PP-seawater (KPP-sw) sorption coefficients at different temperatures, with eight machine learning algorithms. Moreover, novel hybrid intelligent models for predicting Kd more accurately were innovatively developed by applying GA, PSO and AdaBoost algorithms to optimize MLP and ELM models. The results indicated that all three optimization algorithms could improve the robustness and predictability of the standalone MLP and ELM models. In all models trained with KPE-w, KPE-sw, KPVC-w and KPP-sw data sets, GBDT-1 and XGBoost-1 models, MLP-GA-2 and MLP-PSO-2 models, MLR-3 and MLR-4 models performed better in terms of goodness of fit (Radj2: 0.907–0.999), robustness (QBOOT2: 0.900–0.937) and predictability (Rext2: 0.889–0.970), respectively. Analyzing the descriptors revealed that temperature, lipophilicity, ionization potential and molecular size were correlated closely with the adsorption capacity of microplastics to organic pollutants. The proposed QSPR models may assist in initial environmental exposure assessments without imposing heavy costs in the early experimental phase.

Influence of chemical structure of organic micropollutants on the degradability with ozonation

The increasing environmental problems due to various organic micropollutants in water cause the search of suitable additional water treatment methods. Gaining experimental data for the large amount and variety of pollutants would consume a lot of time as well as economic and ecologic resources. An alternative approach is predictive quantitative structure-property relationship (QSPR) modeling, which establishes a correlation between the structural properties of a molecules with a biological, physical, or chemical property. Therefore, in this study, QSPR modeling has been conducted using extensive validation techniques and statistical test to investigate the structural influence on the degradability of organic micropollutants with ozonation. In contrast to most of the other studies, the underlying dataset - rate constants for 92 organic molecules - were obtained under standardized conditions with defined experimental parameters. QSPR modeling was executed using a combination of the software PaDEL for descriptor calculation and QSARINS for the modeling process respecting all five OECD-requirements for applicable QSAR/QSPR-models. The final model was selected using a multi-criteria decision-making tool to evaluate the model quality based on all calculated statistical quality parameters. The model included 10 selected descriptors and fingerprints and showed good regression abilities, predictive power, and stability (R² = 0.8221, CCCtr = 0.9024, Q²loo = 0.7436, R²ext = 0.8420, Q²F1 = 0.8104). The applicability domain of the QSPR model was defined and an interpretation of selected model descriptors has been connected to previous experimental studies. A significant influence of the interpretable descriptors was put into experimental context and compared with previous studies and models. For example, the molar refractivity as a measure of size and polarizability of a molecule and the occurrence of important substructures such as a formamide group seem to decrease the removal rate constant. The contribution of lone electrons entering into resonance as well as the occurrence of fused rings were identified as influences for the increase of the degradability of micropollutants by ozonation.

Exploratory and machine learning analysis of the stability constants of HgII- triazene ligands complexes

Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation ( Q LOO 2 = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation.

Structural features promoting adsorption of contaminants of emerging concern onto TiO2 P25: experimental and computational approaches.

The study of the structural features affecting the adsorption of organics, especially contaminants of emerging concern (CECs), onto TiO2 P25 in aqueous medium has far-reaching implications for the understanding and modification of TiO2 P25 in the roles such asan adsorbent and photocatalyst. The effect of pH and γ(TiO2 P25) as variables on the extent of removal of organics by adsorption on TiO2 P25 was investigated by response surface methodology (RSM) and quantitative structure-property relationship (QSPR) modeling. Experimentally determined coefficients of adsorption were used as responses in RSM, yielding a quadratic polynomial equation (QPE) for each of thestudied organics. Furthermore, coefficients (A, B, C, D, E, and F) obtained from QPEs were used as responses in QSPR modeling to establish their dependence on the structural features of thestudied organics. The functional stability and predictive power of the resulting QSPR models were confirmed with internal and external cross validation. The influence of structural features of organics on the adsorption process is explained by molecular descriptors included in the derived QSPR models. The most influential descriptors on the adsorption of organics on TiO2 P25 are found to be those correlated with ionization potential, molecular mass, and volume, then molecular fragments (e.g., -CH =) and particular topological features such as C and N atoms, or two heteroatoms (e.g., N and N or O and Cl) at certain distance. Derived QSPR models can be considered as robust predictive tools for evaluating efficiency of adsorption processes onto TiO2 P25, providing insights into influential structural features facilitating adsorption process.

Predicting Bile and Lipid Interaction for Drug Substances.

Predicting biopharmaceutical characteristics and food effects for drug substances may substantially leverage rational formulation outcomes. We established a bile and lipid interaction prediction model for new drug substances and further explored the model for the prediction of bile-related food effects. One hundred and forty-one drugs were categorized as bile and/or lipid interacting and noninteracting drugs using 1H nuclear magnetic resonance (NMR) spectroscopy. Quantitative structure-property relationship modeling with molecular descriptors was applied to predict a drug's interaction with bile and/or lipids. Bile interaction, for example, was indicated by two descriptors characterizing polarity and lipophilicity with a high balanced accuracy of 0.8. Furthermore, the predicted bile interaction correlated with a positive food effect. Reliable prediction of drug substance interaction with lipids required four molecular descriptors with a balanced accuracy of 0.7. These described a drug's shape, lipophilicity, aromaticity, and hydrogen bond acceptor capability. In conclusion, reliable models might be found through drug libraries characterized for bile interaction by NMR. Furthermore, there is potential for predicting bile-related positive food effects.

ADMET and quantitative structure property relationship analysis of anti‐Covid drugs against omicron variant with some degree‐based topological indices

AbstractChemical graph theory is one of the important fields in mathematical chemistry which provides a useful tool called topological indices which transforms chemical structure of a molecule into a numerical value. Topological indices are used to investigate physicochemical properties, pharmaco‐kinetic properties, biological activity in quantitative structure property relationship (QSPR) and quantitative structure–activity relationship. The most life‐threatening disease the world facing is COVID‐19 and its prominent variants like alpha, beta, delta and omicron variants. The delta and omicron variants are deemed one of the most contagious strains of the SARS‐CoV‐2 virus. Since there is no exact drug for these variants, but researches are been carried out with some existing and new drugs which are effective against delta and omicron variants. Favipiravir, Baricitinib, Fluvoxamine, Nirmatrelvir and Molnupiravir are some of the drugs currently used to treat COVID‐19 omicron variant. In this paper QSPR model is designed to predict some selected physicochemical properties and ADMET properties of the above aforesaid drugs by using some degree‐based indices such as first K Banhatti index, second K Banhatti index, first K hyper Banhatti index, second K hyper Banhatti index, modified first K Banhatti index, modified second K Banhatti index and harmonic K Banhatti index via M‐Polynomial. The relationship analyses for the physicochemical and ADMET properties with the degree‐based indices are done by using the quadratic and cubic regression method respectively. The results obtained can be expanded to correlate several other properties associated with drugability of potential candidates that can further be utilized to construct a disease‐based library of drugs.

A Developed QSPR Model for the Melting Points of Isatin Derivatives

This paper suggests a developed quantitative structure property relationship (QSPR) model for coping the melting point (M.P) which is considered as the main and important physical property of solid state. The development was based on the decreasing in number of descriptors in order to be statistically intensive with excellent values of statistical parameters. The model was applied successfully to the already published data of M.P for 32 biologically active molecules derived from 4-(1-aryl-2-oxo-1,2-dihydro-indol-3-ylideneamino)-N-substituted benzene sulfonamides. The calculations of descriptors were carried out using density functional theory (DFT) with bases set of 6-311G (d, P). A statistically intensive QSPR model contains only three descriptors with physical meaning has been introduced. Two of them are belonging to the direct theoretical calculations but the third was considered as three dimensional correcting term of which depending on the chemical structure of the substituent. The theoretically calculated descriptors were the total connectivity (TC) and the average charge on the aryl group (AQArH) as both depending on the packing of molecules and responsible on M.P. The last descriptor was suggested as a correction term with respect to the packing of molecules of which depending on their three dimensional chemical structure which only taking the values of -1, 0 and 1. A relatively excellent statistical parameters for the developed model were obtained with square regression coefficient (r2), cross-validation (q2) and root mean squared error (RMSE) are equal to 0.925, 0.903 and 15.26oC, respectively. It was concluded that the developed model gives more confidence results in addition to physical significance which can be considered as a helpful tool for understanding the factors affecting the melting point.

Predicting electrophoretic mobility of TiO2, ZnO, and CeO2 nanoparticles in natural waters: The importance of environment descriptors in nanoinformatics models

Natural and engineered nanoparticles (NPs) entering the environment are influenced by many physicochemical processes and show various behavior in different systems (e.g., natural waters showing different characteristics). Determining the physicochemical characteristics and predicting the behavior of nanoparticles ending up in the natural aquatic environment are key aspects of their risk assessment. Here, we show that the quantitative structure-property relationship modeling method used in nanoinformatics (nano-QSPR) can be successfully applied to predict environmental fate-relevant properties (electrophoretic mobility) of TiO2, ZnO, and CeO2 nanoparticles. However, in contrast to the previous works, we postulate to use, in parallel: (i) the nanoparticles' structure descriptors (S-descriptors) and (ii) the environment descriptors (E-descriptors) as the input variables. Thus, the method should be abbreviated more precisely as nano-QSEPR (“E” stands for the “environment”). As a proof-of-the-concept, we have developed a group of models (including MLR, GA-PLS, PCR, and Meta-Consensus models) with high predictive capabilities (QEXT2 = 0.931 for the GA-PLS model), where the S-descriptors are represented by the core-shell model descriptor and the E-descriptors – by different ambient water features (including ions concentration and the ionic strength). The newly proposed nano-QSEPR modeling scheme can be efficiently used to design safe and sustainable nanomaterials.

Friction sensitivity of nitramine energetic materials: a prediction based on genetic function approximation

In this paper, we report a quantitative structure-property relationship (QSPR) model development for friction sensitivity prediction of nitramine energetic materials. The model is obtained by means of a sophisticated method, namely, genetic function approximation and consists of 10 descriptors. As a training set, we have applied 80 nitramine energetic materials of a few families, both molecular and salt-like. The corresponding test set included 30 compounds. As a result, we have obtained a good correlation with R2 = 0.90. Only two descriptors, the heat of formation and quadrupole components, need semi-empirical quantum-chemical calculations, while the rest ones are so-called “fast descriptors” meaning the relatively short time of the prediction. Using the obtained QSPR model along with our recent method for fast crude estimation of the detonation properties based on empirical formulas we have modified a few energetic salts in order to obtain materials with better friction sensitivity and detonation performance. This was mainly done by changing the constituting ions: thus, 10 nitramine energetic materials with improved characteristics were proposed.

Polymer informatics for QSPR prediction of tensile mechanical properties. Case study: Strength at break.

The artificial intelligence-based prediction of the mechanical properties derived from the tensile test plays a key role in assessing the application profile of new polymeric materials, especially in the design stage, prior to synthesis. This strategy saves time and resources when creating new polymers with improved properties that are increasingly demanded by the market. A quantitative structure-property relationship (QSPR) model for tensile strength at break is presented in this work. The QSPR methodology applied here is based on machine learning tools, visual analytics methods, and expert-in-the-loop strategies. From the whole study, a QSPR model composed of five molecular descriptors that achieved a correlation coefficient of 0.9226 is proposed. We applied visual analytics tools at two levels of analysis: a more general one in which models are discarded for redundant information metrics and a deeper one in which a chemistry expert can make decisions on the composition of the model in terms of subsets of molecular descriptors, from a physical-chemical point of view. In this way, with the present work, we close a contribution cycle to polymer informatics, providing QSPR models oriented to the prediction of mechanical properties related to the tensile test.

First QSPR models to predict the thermal stability of potential self-reactive substances

Self-reactive substances are unstable chemical substances which can easily decompose and may lead to explosion in transport, storage, or process situations. For this reason, their thermal stability properties are required to assess possible process safety issues and for classification purpose. In this study, the first quantitative structure–property relationships (QSPR) dedicated to this class of compounds were developed to predict the heat of decomposition of possible self-reactive substances from their molecular structures. The database used to develop and validate the models was issued from a dedicated experimental campaign on 50 samples using differential scanning calorimetry in homogeneous experimental conditions. QSPR models were derived using the GA-MLR methods (using a genetic algorithm and multi-linear regressions) using molecular descriptors calculated by Dragon software based on two types of inputs: 3D molecular structures determined using the density functional theory (DFT), allowing access to three-dimensional descriptors, and from SMILES codes, favoring the access to simpler models, requiring no preliminary quantum chemical calculations. All models respected the OECD validation guidelines for regulatory acceptability of QSPR models. They were tested by internal and external validation tests and their applicability domains were defined and analyzed.

QSPR Modeling with Topological Indices of Some Potential Drug Candidates against COVID-19

COVID-19, which has spread all over the world and was declared as a pandemic, is a new disease caused by the coronavirus family. There is no medicine yet to prevent or end this pandemic. Even if existing drugs are used to alleviate the pandemic, this is not enough. Therefore, combinations of existing drugs and their analogs are being studied. Vaccines produced for COVID-19 may not be effective for new variants of this virus. Therefore, it is necessary to find the drugs for this disease as soon as possible. Topological indices are the numerical descriptors of a molecular structure obtained by the molecular graph. Topological indices can provide information about the physicochemical properties and biological properties of molecules in the quantitative structure-property relationship (QSPR) and quantitative structure-activity relationship (QSAR) studies. In this paper, some analogs of lopinavir, favipiravir, and ritonavir drugs that have the property of being potential drugs against COVID-19 are studied. QSPR models are studied using linear and quadratic regression analysis with topological indices for enthalpy of vaporization, flash point, molar refractivity, polarizability, surface tension, and molar volume properties of these analogs.

A simple, robust, and efficient structural model to predict thermal stability of zinc metal-organic frameworks (Zn-MOFs): The QSPR approach

The thermal stability of MOFs refers to resist degradation of their structure, upon exposure to heat. It may depend on the type of linkers, metal SBUs, metal-ligand interaction, and type of packing of MOF. Many applications of MOF compounds require high thermal stability so that their frameworks do not collapse upon thermal treatment. A straightforward method based on a Quantitative Structure-Property Relationship (QSPR) was established to achieve a precise model to estimate the thermal stability of MOF compounds having zinc SBUs due to their importance in recent researches. The proposed model was developed based on descriptors including the number of nitrogen and zinc atoms, the interactions between heteroatoms of linkers and metal centers, and various molecular fragments. A logarithm of experimental thermal stability (log TS) of 151 MOFs is applied to develop a precise model. The coefficients of determination (R 2 ) for the training and test sets were obtained 0.999 and 1.000, respectively. The Root Mean Square Error for Prediction (RMSEP) of log TS of training and test sets are 0.003 and 0.001, respectively. According to resulted statistical parameters, the novel model is robust and efficient satisfactory. • The thermal stability of metal-organic frameworks (MOFs) is one of the principal concerns for various applications. • A QSPR model was developed to simply predict the relationship between thermal stability and the Zn-MOF SBUs. • The QSPR approach was reliable, robust, and applicable for Zn-MOFs with diverse linkers. • Considering the resulted multilinear regression model, MOF compounds can be well designed with suitable thermal stability. • The presented QSPR model will be developed to estimate other physicochemical properties of MOFs from their experimental data.

Predicting Primary Biodegradation of Petroleum Hydrocarbons in Aquatic Systems: Integrating System and Molecular Structure Parameters using a Novel Machine-Learning Framework.

Quantitative structure–property relationship (QSPR) models for predicting primary biodegradation of petroleum hydrocarbons have been previously developed. These models use experimental data generated under widely varied conditions, the effects of which are not captured adequately within model formalisms. As a result, they exhibit variable predictive performance and are unable to incorporate the role of study design and test conditions on the assessment of environmental persistence. To address these limitations, a novel machine‐learning System‐Integrated Model (HC‐BioSIM) is presented, which integrates chemical structure and test system variability, leading to improved prediction of primary disappearance time (DT50) values for petroleum hydrocarbons in fresh and marine water. An expanded, highly curated database of 728 experimental DT50 values (181 unique hydrocarbon structures compiled from 13 primary sources) was used to develop and validate a supervised model tree machine‐learning model. Using relatively few parameters (6 system and 25 structural parameters), the model demonstrated significant improvement in predictive performance (root mean square error = 0.26, R 2 = 0.67) over existing QSPR models. The model also demonstrated improved accuracy of persistence (P) categorization (i.e., “Not P/P/vP”), with an accuracy of 96.8%, and false‐positive and ‐negative categorization rates of 0.4% and 2.7%, respectively. This significant improvement in DT50 prediction, and subsequent persistence categorization, validates the need for models that integrate experimental design and environmental system parameters into biodegradation and persistence assessment. Environ Toxicol Chem 2022;41:1359–1369. © 2022 ExxonMobil Biomedical Sciences, Inc. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

QSPR Modelling of the Solubility of Drug and Drug-like Compounds in Supercritical Carbon Dioxide.

Quantitative structure-property relationship (QSPR) modeling was investigated to predict drug and drug-like compounds solubility in supercritical carbon dioxide. A dataset of 148 drug\drug-like compounds, accounting for 3971 experimental data points (EDPs), was collected and used for modelling the relationship between selected molecular descriptors and solubility fraction data achieved by a nonlinear approach (Artificial neural network, ANN) based on molecular descriptors. Experimental solubility data for a given drug were published as a function of temperature and pressure. In the present study, 11 significant PaDEL descriptors (AATS3v, MATS2e, GATS4c, GATS3v, GATS4e, GATS3 s, nBondsM, AVP-0, SHBd, MLogP, and MLFER_S), the temperature and the pressure were statistically proved to be sufficient inputs. The architecture of the optimized model was found to be {13,10,1}. Several statistical metrics, including average absolute relative deviation (AARD=3.7748 %), root mean square error (RMSE=0.5162), coefficient of correlation (r=0.9761), coefficient of determination (R2 =0.9528), and robustise (Q2 =0.9528) were used to validate the obtained model. The model was also subjected to an external test by using 143 EDPs. Sensitivity analysis and domain of application were examined. The overall results confirmed that the optimized ANN-QSPR model is suitable for the correlation and prediction of this property.

Quantitative Structure-property Relationship Model Based on Artificial Neural Network

Artificial neural network (ANN) has been widely researched and applied in chemical process, because of its parallel processing and excellent nonlinear mapping ability, with strong robustness and fault tolerance. By using artificial neural network to establish the model between the properties of mixture and its molecular structure, more accurate data can be predicted and obtained than those determined by experiment. This paper summarizes the development process of artificial neural network and analyzes the application of ANN in quantitative structure-property relationship model (QSPR). It is pointed out that QSPR model combined with artificial neural network can effectively predict the properties of compounds or mixtures, which can shorten the experimental testing process and is able to be widely used with less limitation. It has important significance in application of new biomass fuel, the analysis of the pollution, prediction of the risk of dangerous chemical properties and so on. In the future, there will be broader application space of ANN-QSPR model.

Gradient Boosted Tree model: A fast track tool for predicting the Atmospheric Pressure Chemical Ionization-Mass Spectrometry signal of antipsychotics based on molecular features and experimental settings

Predicting the response signal in Atmospheric Pressure Chemical Ionization - Mass Spectrometry (APCI-MS) systems appears to be considerably challenging due to a gap in knowledge of governing factors and nature of their relationship with response. In this regard, signal intensity is optimized for each analyte separately through trial-and-error approach which impairs the method development and depletes numerous resources.To tackle the given issue, here we proposed the Quantitative Structure - Property Relationship (QSPR) model that estimated the ion signal based on molecular descriptors of tested compounds. In particular, the QSPR model was developed using APCI-MS data acquired for 8 chemical compounds under 41 different experimental conditions. Antipsychotics, namely, sulpiride, risperidone, aripiprazole, bifeprunox, ziprasidone and its three impurities, were selected as model substances to undergo APCI ionization. Experimental (instrumental and solvent-related) parameters were varied according to the scheme of Box-Behnken Design. Gradient Boosted Trees (GBT) technique was used to model sophisticated inputs – output relationships of the monitored system.The GBT algorithm with optimized hyper-parameters (16 estimators, learning rate set to 0.55 and maximal depth set to 7) built a so-called mixed model that yielded satisfactory predictive performance (Root Mean Square Error of Prediction: 5.98%; coefficient of determination: 97.1%). According to the built-in feature selection method, GBT identified experimental factors impacting nebulization and vaporization efficiency, i.e. descriptors related to hydrophobicity and molecular polarizability as the major determinants of observed APCI behavior. Therefore, the proposed model has shed light on the parameters and factors’ interactions that govern the generation of APCI ion signals for the analytes with diverse physical-chemical properties. The established QSPR patterns could be reliably used to predict APCI-MS signal in a variety of experimental environments.

Systematic diesel molecular performance evaluation based on quantitative structure-property relationship model

The production and separation of optimal molecules in diesel fuels require a systematic property evaluation for the containing molecules. This paper evaluates the diesel molecules based on four key quality indicators: low-temperature fluidity, cleanliness, ignition, and power performance. We established the corresponding quantitative structure-property relationship models for corresponding properties, which are freezing point, yield sooting index, cetane number, and combustion heat. The models were applied for the screening of the high-performance molecules that is suitable for diesel. The molecular performance distribution of the conventional diesel and biodiesel were also compared. Moreover, we analyzed the effect of different transformation paths on molecular properties, giving guidance on the conversion process design.