Dragon Software Research Articles

Larvicidal activity prediction of Essential oils against Culex pipiens pallens using QSAR Modeling

The search for an eco-freindly larvicide suitable for vector control requires a budget and considerable time to carry out experiments. Fortunately, the use of QSAR modeling allows the prediction of larvicidal activity of structurally diverse chemicals against mosquitoes in a way quick and costless. This approach can be helpful to study for making biolarvicide with highest ability to destroy mosquito larvae. We propose a QSAR model using two different statistical methods, multiple linear regression (MLR) and Support vector machine (SVM) for predicting the larvicidal activity of 30 compounds of essential oil (EOs) isolated from the root of Asarum heterotropoides against Culex pipiens pallens. A model with four theoretical descriptors derived from Dragon software was developed applying the genetic algorithm (GA)-variable subset selection (VSS) procedure. The statistical parameters, R2 = 0.9716, Q2LOO = 0.9595, s = 0.1690 of the model developed by MLR showed a good predictive capability for log LC50 values. The comparison between the results of SVM and MLR models showed that the SVM model present a good alternative to construct a QSAR model for the prediction of the larvicidal activity.

Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches

Abstract QSPR is a powerful tool for elucidating the correlation between chemical structure and property for both natural and synthesized compounds. In the present work, the half-wave reduction potential for a set of aziridinylquinones (Anticancer Agents [AA]) is modelled using a quantitative structure-electrochemistry relationship (QSER) based on multilinear regression (MLR) and artificial neural network (ANN). Molecular descriptors introduced in this work were computed using the Dragon software (V5). Before the model’s generation, using the Kennard and Stone algorithm, the data set of 84 aziridinylquinones was divided into training and prediction sets consisting of 70 % and 30 % of data points. Quantitative Structure Electrochemistry Relationship (QSER) models were developed using the Genetic Algorithm Multiple Linear Regressions (GA-MLR) and an Artificial Neural Network (ANN). The coefficient of determination (R 2) and Root Mean Squared Error of prediction (RMSE) were mentioned to demonstrate the QSER model’s prediction abilities. Calculated R 2 and RMSEval values for the MLR model were 0.858 and 0.054, respectively. The R 2 and RMSEval values for the ANN training set were calculated to be 0.914 and 0.050, respectively. Findings show that GA is a powerful tool for selecting variables in QSER analysis. Comparing the two employed regression methods showed that ANN is superior to MLR in predictive ability.

Comparative QSAR Modeling for Predicting Anticancer Potency of Imidazo[4,5-b]Pyridine Derivatives Using GA-MLR and BP-ANN Techniques

Background: Prediction of toxicity of imidazo[4,5-b]pyridine derivatives is carried out using GA-MLR and BPANN methods. Objective: A quantitative structure-property relationship (QSPR) was determined based on methods, including genetic algorithm-multiple linear regression (GA-MLR) and backpropagation artificial neural network (BP-ANN). These methods were employed for modeling and predicting the anticancer potency of imidazo[4,5-b]pyridine derivatives. Materials and Methods: A dataset of imidazo[4,5-b]pyridine derivatives was randomly divided into two groups, training and test sets consisting of 75% and 25% of data points, respectively. The optimized conformation of compounds was obtained using the DFT-B3LYP method and 6-31G* basis sets level with Gaussian 09 software. A large number of molecular descriptors were calculated using Dragon software. The QSAR models were optimized using multiple linear regressions (MLR). Results: The most relevant molecular descriptors were obtained using the genetic algorithm (GA) and backward stepwise regression. The predictive powers of the GA-MLR models were studied using leaveone- out (LOO) cross-validation and an external test set. Conclusion: The obtained results of statistical parameters showed the BP-ANN model to have better performance compared to the GA-MLR model. To assess the predictive ability of QSAR models, many statistical terms, such as correlation coefficient (R2), leave-one-out cross-validation (LOOCV), root mean squared error (RMSE), and external and internal validation were used. The results of validation methods demonstrate the QSAR model to be robust and with high predictivity.

Structure-based modeling of critical micelle concentration (CMC) of anionic surfactants in brine using intelligent methods

Critical micelle concentration (CMC) is one of the main physico-chemical properties of surface-active agents, also known as surfactants, with diverse theoretical and industrial applications. It is influenced by basic parameters such as temperature, pH, salinity, and the chemical structure of surfactants. Most studies have only estimated CMC at fixed conditions based on the surfactant’s chemical parameters. In the present study, we aimed to develop a set of novel and applicable models for estimating CMC of well-known anionic surfactants by considering both the molecular properties of surfactants and basic affecting factors such as salinity, pH, and temperature as modeling parameters. We employed the quantitative-structural property relationship technique to employ the molecular parameters of surfactant ions. We collected 488 CMC values from literature for 111 sodium-based anionic surfactants, including sulfate types, sulfonate, benzene sulfonate, sulfosuccinate, and polyoxyethylene sulfate. We computed 1410 optimized molecular descriptors for each surfactant using Dragon software to be utilized in the modelling processes. The enhanced replacement method was used for selecting the most effective descriptors for the CMC. A multivariate linear model and two non-linear models are the outputs of the present study. The non-linear models were produced using two robust machine learning approaches, stochastic gradient boosting (SGB) trees and genetic programming (GP). Statistical assessment showed highly applicable and acceptable accuracy of the newly developed models (RSGB2 = 0.999395 and RGP2 = 0.954946). The ultimate results showed the superiority and greater ability of the SGB method for making confident predictions.

Modeling of Inhibition of Tetrahymena pyriformis growth by Aliphatic Alcohols and Amines pollution of l’ environmental

To assess the relative toxicity of a mixed series of 21(linear and branched-chain) alcohols and 9 normal aliphatic amines in terms of the 50% inhibitory growth concentration (IGC50) of Tetrahymena Pyriformis, a Quantitative Modeling study know as a Structure-Activity/property/Toxicity Relationship (QSAR/QSPR/QSTR) was conducted (20 training,10 tests). The used least squares LS method has been using MINITAB 16 Software and nom-parametric estimation (least absolute deviation LAD) (robust regression method) has been using Calculation Programs by MATLAB Software. The applied simple linear regression approach is based on theoretical H4p (GETAWAY descriptor) molecular descriptor from DRAGON software The performance of regression is better if the distribution of errors has normal, in this case we use the least squares LS method for statistical analysis. When the data does not have a natural assumption, we move to another method of analysis that is more robust and more frequent for the presence of the points of articulation, which is the least absolut deviation method (LAD). The findings of statistical analysis for the chosen model (QSAR) using simple linear Regression using the least Squares Method were R^2=97.39% ,Q^2=96.69% ,Q_bOOT^2=96.24%,Q_EXT^2=93.91% ,R_adj^2=97.24%, S=0.248 Anderson Darling (AD) test =1.57 >0.752 , symmetry coefficient (ou skeweness) (sk= 2.14>0 ) , flatness coefficient (Kurtosis) (ku=5.75>3) and Jarque and Bera Test (JB= 42.84>5.9942. the results did not follow the normal law (unnormal). The coefficient of determination and the value of standard deviation are both highly sensitive to the presence of aberrant compounds(abnormales), as the R^2value moved from 87, 96 % to 94.18 %, which increased by a value of 6.22% and the value of standard deviation (S) moved from 0.399 to 0.303, it increased by a value of 25 % after removing aberrant compound (abnormalie) are interpreted as better adjustment and they are positively. After removing the aberrant compound, we did not see any change in the lines coefficients, indicatting that the function’s graph is stable, demonstrating the LAD method and increased power, which are unaffected by the presence of aberrant compounds Consequently, which means that the model of one descriptor selected is good and statistically strong, Three influential compounds detected ((one compound of training, two compounds of Test) and important the model and absence of studied sample aberrants compounds.

Prediction of Partition Coefficient of Carbamates using GA-MLR and GAANN Methods, and Comparison with Experimental Data

Abstract: In the present study, quantum mechanics calculations at the B3LYP theory level and 6- 31G* basis set were carried out to obtain the optimized geometry of carbamates. Then, a comprehensive set of molecular descriptors was computed by using the Dragon software. A genetic algorithm (GA) was also applied to select the suitable variables that resulted in the best-fixed models. The relationship between the molecular descriptors and the partition coefficient of 66 types of carbamates is represented. The molecular descriptors were applied for modeling the multiple linear regression (MLR) and artificial neural network (ANN) methods. The quantitative structure-property relationship models showed that the GA-ANN over the GA-MLR approach resulted in the best outcome. So, the predicted partition coefficient was found to be in good agreement with the experimental partition coefficient. The EEig01x and ALOGP descriptors were applied for modeling the multiple linear regression (MLR) and artificial neural network (ANN) methods. The best model was validated by Q2 LOO, Q2 F1, Q2 F2, Q2 F3, and CCC techniques and external validation parameters for the established theoretical models.

Quantitative Structure-Retention Relationship Analysis of Polycyclic Aromatic Compounds in Ultra-High Performance Chromatography.

A comparative quantitative structure-retention relationship (QSRR) study was carried out to predict the retention time of polycyclic aromatic hydrocarbons (PAHs) using molecular descriptors. The molecular descriptors were generated by the software Dragon and employed to build QSRR models. The effect of chromatographic parameters, such as flow rate, temperature, and gradient time, was also considered. An artificial neural network (ANN) and Partial Least Squares Regression (PLS-R) were used to investigate the correlation between the retention time, taken as the response, and the predictors. Six descriptors were selected by the genetic algorithm for the development of the ANN model: the molecular weight (MW); ring descriptor types nCIR and nR10; radial distribution functions RDF090u and RDF030m; and the 3D-MoRSE descriptor Mor07u. The most significant descriptors in the PLS-R model were MW, RDF110u, Mor20u, Mor26u, and Mor30u; edge adjacency indice SM09_AEA (dm); 3D matrix-based descriptor SpPosA_RG; and the GETAWAY descriptor H7u. The built models were used to predict the retention of three analytes not included in the calibration set. Taking into account the statistical parameter RMSE for the prediction set (0.433 and 0.077 for the PLS-R and ANN models, respectively), the study confirmed that QSRR models, associated with chromatographic parameters, are better described by nonlinear methods.

Support Vector Machine-Based Global Classification Model of the Toxicity of Organic Compounds to Vibrio fischeri.

Vibrio fischeri is widely used as the model species in toxicity and risk assessment. For the first time, a global classification model was proposed in this paper for a two-class problem (Class - 1 with log1/IBC50 ≤ 4.2 and Class + 1 with log1/IBC50 > 4.2, the unit of IBC50: mol/L) by utilizing a large data set of 601 toxicity log1/IBC50 of organic compounds to Vibrio fischeri. Dragon software was used to calculate 4885 molecular descriptors for each compound. Stepwise multiple linear regression (MLR) analysis was used to select the descriptor subset for the models. The ten molecular descriptors used in the classification model reflect the structural information on the Michael-type addition of nucleophiles, molecular branching, molecular size, polarizability, hydrophobic, and so on. Furthermore, these descriptors were interpreted from the point of view of toxicity mechanisms. The optimal support vector machine (SVM) model (C = 253.8 and γ = 0.009) was obtained with the genetic algorithm. The SVM classification model produced a prediction accuracy of 89.1% for the training set (451 log1/IBC50), of 80.0% for the test set (150 log1/IBC50), and of 86.9% for the total data set (601 log1/IBC50), which are higher than that (80.5%, 76%, and 79.4%, respectively) from the binary logistic regression (BLR) model. The global SVM classification model is successful, although it deals with a large data set in relation to the toxicity of organics to Vibrio fischeri.

CP-MLR/PLS directed quantitative structure-activity relationship study on the histamine H3 receptor binding affinity: The cyclohexylamine based series

The histamine H3 receptor binding affinities of cyclohexylamine derivatives has been analysed with the topological and molecular features from Dragon software. Analysis of the structural features in conjunction with the biological endpoints in combinatorial protocol in multiple linear regression (CP-MLR) led to the identification of 26 descriptors for modelling the activity. The study clearly suggested the role of atomic properties such as mass, electronegativity or charge content, polarizability, atomic van der Waals volume, average valence connectivity index chi-5 and absence of number of acceptor atoms for H-bonds (N, O, F) type functionality to optimise the histamine H3 receptor binding affinity of titled compounds. The models developed and the participating descriptors advocate that the substituent groups of the cylohexylamine moiety hold scope for further modification in the optimization of the H3 receptor binding affinity. Analysis of these descriptors in partial least squares (PLS) highlighted their relative significance in modulating the biological response. The selected descriptors are enriched with information corresponding to the activity when compared to the remaining ones. Applicability domain analysis revealed that the suggested model matches the high quality parameters with good fitting power and the capability of assessing external data and all of the compounds was within the applicability domain of the proposed model and were evaluated correctly.

QSAR Studies on Some Sulfonamides as Antidiabetic Agents

The need for antidiabetic agents is growing day by day in pharmaceutical industry to find novel, potent and more specialized class of medicine. To address the need we have evaluated a set of sulphonamide drugs for their potential to fight against diabetes. In the present study antidiabetic activity of 47 sulfonamide derivatives have been modeled using topological descriptors. The activity in terms of pKi have been modeled using descriptors calculated from Dragon software. The best model having R2 value 0.9897 have been reported after deleting two outliers. The model was tested using Cross validated method. The R2cv value for the reported five-parametric model comes out to be 0.9896. The model has also been tested for collinearity or defect of Chance. It has been established that the model is free from any defect. The author suggests the testing of these compound for in vivo studies to specify the other pharmacological significance and therapeutic potential for future use.

Predicting rejection of emerging contaminants through RO membrane filtration based on ANN-QSAR modeling approach: trends in molecular descriptors and structures towards rejections.

In this work, a quantitative structure-activity relationship (QSAR) study was performed on a set of emerging contaminants (ECs) to predict their rejections by reverse osmosis membrane (RO). A wide range of molecular descriptors was calculated by Dragon software for 72 ECs. The QSAR data was analyzed by an artificial neural network method (ANN), in which four out of 3000 theoretical molecular descriptors were chosen and their significance was computed based on the Garson method. The significance trends of descriptors were as follows in descending order: ESpm14u > R2e > SIC1 > EEig03d. The selected descriptors were ranked based on their importance and then an explorative study was conducted on the QSAR data to show the trends in molecular descriptors and structures toward the rejections values of ECs. The MLR algorithm was used to make a linear model and the results were compared with those of the nonlinear ANN algorithm. The comparison results revealed it is necessary to apply the ANN model to this data with non-linear properties. For the whole dataset, the correlation coefficient (R2) and residual mean squared error (RMSE) of the ANN and MLR methods were 0.9528, 6.4224; and 0.8753, 11.3400, respectively. The comparison results showed the superiority of ANN modeling in the analysis of ECs' QSAR data.

Boosting Speech-to-Text software potential

The article focuses on finding ways of boosting efficiency and accuracy of Speech-to-Text (STT)-powered input. The effort is triggered by the growing popularity of the software among professional translators, which is in line with the general trend of abandoning typing in favor of speech-to-text applications. Insisting that better effectiveness of such programs is contingent on their accuracy, the researchers analyze major factors, both linguistic and technical in nature, affecting the computer-assisted speech transcribing quality. This leads to an experiment, putting the hypothesis to a test. Based on numerical and performance data, errors and their breakdown into categories in an attempt to figure out their origins, it dwells on various approaches to dictation in a combination with several hardware options and configurations. These pave the way for recommendations on the improvement of STT performance based on the Dragon software. The authors arrive at a conclusion that it is possible to boost the STT accuracy up to 99 percent by adjusting the program profile to accommodate phonetic features of the speaker with due consideration of his accent, adding to the dictionary the most complex and rare vocabulary beforehand, and fine-tuning input hardware. Other noteworthy results include ways to overcome the most complex transcribing challenges, i.e. proper names, placenames, abbreviations, etc.

Comparative QSAR modeling of 2‐phenylindol derivatives for predicting the anticancer activity using genetic algorithm multiple linear regression and back‐propagation‐artificial neural network techniques

AbstractQuantitative structure–activity relationship (QSAR) studies on a series of 2‐phenylindole derivatives as anticancer drugs were performed to choice the important descriptor, which is responsible for their anticancer activity (expressed as pIC50). The geometry optimizations were performed on the structures using Gaussian software with the density functional B3LYP and 6‐311G(d,p) basis sets. Dragon software was used to calculate molecular descriptors, and the genetic algorithm (GA) procedure and backward regression were used to proper selection of the most relevant descriptors. The backward multiple linear regression (BW‐MLR) and backpropagation‐artificial artificial neural network (BP‐ANN) were carried out to design QSAR models. The squared correlation coefficient (R2) and the root mean squared error (RMSE) values of the GA‐MLR model were calculated to be 0.2843 and 0.7001, respectively. The BP‐ANN model was the most powerful, with the square of predictive correlation coefficient R2pred, root mean square error (RMSE), and absolute average deviation (AAD) which was equal to 0.9416, 0.0238, and 0.0099, respectively.

QSAR study of indole derivatives as active agents against Candida albicans: a DFT calculation

Indole and its derivatives are common heterocyclic compounds in nature that have a wider range of medicinal activities such as antifungal, anti-inflammatory, and anti-seizure. Virtually all indole derivatives showed outstanding antifungal activity against Candida albicans. The aim of this study was to QSAR modeling of indole derivatives and the design of new drugs that have antifungal activity. In this study, 52 compounds were selected. All optimized compounds and quantum descriptors were obtained using Gaussian software and DFT/B3LYP computational method with 6–31 G (d) basis set al, so other descriptors were determined using Dragon software. To examine the relationship between these descriptors and the activity of these compounds, the MLR linear correlation method was used, and the QSAR equation with R2 = 0.7884 and R = 0.8879 was obtained for it. Likewise, MSE = 0.1897, RMSE = 0.2848, and Q2 = 0.68663 approve the acceptability of the obtained model. The obtained equation reveals that the activity of these compounds is related to the negative coefficient of GATS8p, R7e +, and G2e, which means that with increasing the values ​​of these description nodes, the amount of activity declines. On the other hand, the activity of these compounds depended on the positive coefficients of HATS3p, MATS5e, and RDF045, i.e. with increasing these values, the activity of these compounds also increases, and a good correlation was obtained between the experimental and predicted activity values.

QSPR models to predict quantum chemical properties of imidazole derivatives using genetic algorithm–multiple linear regression and back‐propagation–artificial neural network

AbstractImidazole derivatives are the foundation of different types of drugs with a wide range of biological activities. In this study, the genetic algorithm–multiple linear regression (GA–MLR), and backpropagation–artificial neural network (BP–ANN) were applied to design QSPR models to predict the quantum chemical properties like the entropy (S) and enthalpy of formation (∆Hf) of imidazole derivatives. In order to draw molecular structure of 84 derivative compounds Gauss View 05 program was used. These structures were optimized at DFT‐B3LYP/6‐311G* level with Gaussian09W. The Dragon software was used to calculate a set of different molecular descriptors, and the genetic algorithm procedure and backward stepwise regression were applied for the selection of descriptors. The resulting quantitative GA–MLR model of ∆Hf, showed that there is good linear correlation between the selected descriptors and ∆Hf of compounds. Also the results show that the BP–ANN model appeared to be superior to GA–MLR model for prediction of entropy. Different internal and external validation metrics were adopted to verify the predictive performance of QSPR models. The predictive powers of the models were found to be acceptable. Thus, these QSPR models may be useful for designing new series of imidazole derivatives and prediction of their properties.

Comparison of the clinical effect features of Han-Ku-Gan and Wen-Xin-Gan based on the efficacy of promoting blood circulation and removing blood stasis

The application of Chinese materia medica (CMM) in clinical diseases is the embodiment and continuation of the property theory of CMM (PTCMM). However, due to a lack of precise quantitative description methods, it is difficult to systematically analyze the property of CMM (PCMM) and clinical effect features at the micro molecular level. The therapeutic drugs and targets were obtained from the Drugbank database. The molecular descriptors of these drugs were calculated based on Dragon software. Drug–effect relationships that integrated the molecular descriptors and effect descriptors were plotted as grayscale images. These images were used to train the LeNet-5 model and the AlexNet model. The best-performing model was used to predict the effect features of the CMM compounds. Finally, the effect features of the PCMM combinations were calculated based on the support vector machine recursive feature elimination algorithm. The AlexNet model showed a superior prediction performance. The results showed that its accuracy, precision, sensitivity, F-measure, and Matthews correlation coefficient on the training set were 0.940, 0.936, 0.945, 0.940, and 0.880, respectively, and those of the test set were 0.909, 0.901, 0.920, 0.910, and 0.819, respectively. A total of 399 compounds in the 42 CMMs for promoting blood circulation and removing blood stasis were predicted by this model. The key effect features of the Han - Ku - Gan combination were anti-inflammatory, anti-tumor, anti-atherosclerosis, anti-Parkinson, hypoglycemic, and anti-coagulant properties, as well as excitation of uterine smooth muscle. The key effect features of the Wen - Xin - Gan combination were anti-inflammatory, anti-atherosclerosis, anti-hypertensive, anti-coagulant, anti-tumor, and anti-cardiac insufficiency effects, as well as enhanced immunity, sedation and hypnosis, and analgesia. This study provides a new method for the further exploration of the relationship between the PCMM and clinical effect features.

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.

Nitroaromatics as hypoxic cell radiosensitizers: A 2D-QSAR approach to explore structural features contributing to radiosensitization effectiveness

Hypoxia is the prime component of tumor microenvironment that plays a pivotal role in cancer progression. Nitroaromatic compounds are known to enhance the sensitivity of hypoxic cells to ionizing radiation. The application of computational tools like Quantitative Structure-Activity Relationship (QSAR) can be used to predict newly developed nitroaromatics or compounds with missing data. In the present work, three datasets consisting of 18 nitrofurans, 11 nitrothiophenes and 84 nitroimidazoles were utilised for two-dimensional QSAR modeling to retrieve their structural features essential to elicit radiosensitivity. The work comprises two parts: (i) local modeling using individual datasets; and (ii) global modeling by clubbing the three datasets. The two-dimensional descriptors were calculated using Dragon (version 7.0) software. The developed models were obtained using various feature selection techniques applied in “Small Dataset Modeling” and “Double Cross Validation” tools available from https://dtclab.webs.com/software-tools. Finally, the models were validated using stringent metrics following the Organisation for Economic Co-operation and Development (OECD) guidelines. The developed models are robust, predictive, and are useful tools to predict the radiosensitization of newly developed nitroaromatics. Furthermore, the global model was used to predict two external sets comprising 10 and 47 compounds, and the prediction ability was validated using the “Prediction Reliability Indicator” tool.

Ligand-based discovery of new potential acetylcholinesterase inhibitors for Alzheimer’s disease treatment

ABSTRACT The enzyme acetylcholinesterase (AChE) is currently a therapeutic target for the treatment of neurodegenerative diseases. These diseases have highly variable causes but irreversible evolutions. Although the treatments are palliative, they help relieve symptoms and allow a better quality of life, so the search for new therapeutic alternatives is the focus of many scientists worldwide. In this study, a QSAR-SVM classification model was developed by using the MATLAB numerical computation system and the molecular descriptors implemented in the Dragon software. The obtained parameters are adequate with accuracy of 88.63% for training set, 81.13% for cross-validation experiment and 81.15% for prediction set. In addition, its application domain was determined to guarantee the reliability of the predictions. Finally, the model was used to predict AChE inhibition by a group of quinazolinones and benzothiadiazine 1,1-dioxides obtained by chemical synthesis, resulting in 14 drug candidates with in silico activity comparable to acetylcholine.

QSPR study on thermal energy of aliphatic Aldehydes using molecular descriptors and MLR technique

A QSPR study method is applied for the physico- chemical property represented by thermal Energy of aliphatic Aldehydes, by the employment of the following molecular descriptors blocks molecular properties, constitutional descriptors, charge descriptors, which represent the molecular structure, where it was calculated using Dragon software. The idea behind the choice of these physico- chemical descriptors is to use realistic molecular quantities. The thermal Energy values for a set of 24 aliphatic Aldehydes were used for doing QSPR analysis and a group of multiple linear regression (MLR) models. The analysis of computed data and test of model for a validation set including 30% of compounds shows that selected descriptors have a good correlation with thermal Energy(Eth). The results have shown that combining descriptors could be used successfully for modeling and predicting the thermal energy.