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Journal articles on the topic 'QSAR Model'

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Published: 18 May 2024

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1

Li, Yan Kun, and Xiao Ying Ma. "QSAR/QSPR Model Research of Complicated Samples." Advanced Materials Research 740 (August 2013): 306–9. http://dx.doi.org/10.4028/www.scientific.net/amr.740.306.

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QSAR/QSPR study is a hot issue in present chemical informatics research, and is the very active research domain. In present, a large number of QSAR/QSPR (quantitative structure-activity/property relationships) models have been widely studied and applied in a lot of different areas. This paper overviews the developments, research methods and applications of QSAR/QSPR model.
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Okey, Robert W., and H. David Stensel. "A QSAR-based biodegradability model—A QSBR." Water Research 30, no. 9 (September 1996): 2206–14. http://dx.doi.org/10.1016/0043-1354(96)00098-x.

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3

Zhang, Xiujun, H. G. Govardhana Reddy, Arcot Usha, M. C. Shanmukha, Mohammad Reza Farahani, and Mehdi Alaeiyan. "A study on anti-malaria drugs using degree-based topological indices through QSPR analysis." Mathematical Biosciences and Engineering 20, no. 2 (2022): 3594–609. http://dx.doi.org/10.3934/mbe.2023167.

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<abstract> <p>The use of topological descriptors is the key method, regardless of great advances taking place in the field of drug design. Descriptors portray the chemical characteristic of a molecule in numerical form, that is used for QSAR/QSPR models. The numerical values related with chemical constitutions that correlates the chemical structure with the physical properties referto topological indices. The study of chemical structure with chemical reactivity or biological activity is termed as quantitative structure activity relationship, in which topological index play a significant role. Chemical graph theory is one such significant branches of science which play a key role in QSAR/QSPR/QSTR studies. This work is focused on computing various degree-based topological indices and regression model of nine anti-malaria drugs. Regression models are fitted for computed indices values with 6 physicochemical properties of the anti-malaria drugs are studied. Based on the results obtained, an analysis is carried out for various statistical parameters for which conclusions are drawn.</p> </abstract>
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4

Toropov, Andrey A., and Alla P. Toropova. "The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR." Current Computer-Aided Drug Design 16, no. 3 (June 2, 2020): 197–206. http://dx.doi.org/10.2174/1573409915666190328123112.

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Background: The Monte Carlo method has a wide application in various scientific researches. For the development of predictive models in a form of the quantitative structure-property / activity relationships (QSPRs/QSARs), the Monte Carlo approach also can be useful. The CORAL software provides the Monte Carlo calculations aimed to build up QSPR/QSAR models for different endpoints. Methods: Molecular descriptors are a mathematical function of so-called correlation weights of various molecular features. The numerical values of the correlation weights give the maximal value of a target function. The target function leads to a correlation between endpoint and optimal descriptor for the visible training set. The predictive potential of the model is estimated with the validation set, i.e. compounds that are not involved in the process of building up the model. Results: The approach gave quite good models for a large number of various physicochemical, biochemical, ecological, and medicinal endpoints. Bibliography and basic statistical characteristics of several CORAL models are collected in the present review. In addition, the extended version of the approach for more complex systems (nanomaterials and peptides), where behaviour of systems is defined by a group of conditions besides the molecular structure is demonstrated. Conclusion: The Monte Carlo technique available via the CORAL software can be a useful and convenient tool for the QSPR/QSAR analysis.
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Mudasir, Mudasir, Iqmal Tahir, and Ida Puji Astuti Maryono Putri. "QUANTITATIVE STRUCTURE AND ACTIVITY RELATIONSHIP ANALYSIS OF 1,2,4-THIADIAZOLINE FUNGICIDES BASED ON MOLECULAR STRUCTURE CALCULATED BY AM1 METHOD." Indonesian Journal of Chemistry 3, no. 1 (June 7, 2010): 39–47. http://dx.doi.org/10.22146/ijc.21904.

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Quantitative structure-Activity relationship (QSAR) analysis of fungicides having 1,2,4-thiadiazoline structure based on theoretical molecular properties have been done. Calculation of the properties was conducted by semiempirical method AM1 and the activity of the compounds was taken from literature. Relationship analysis between fungicides activity (pEC50) and molecular properties was done using SPSS program. The QSAR analysis gave the best model as follows: pEC50 = 3.842 + (1.807x10-4) ET + (5.841x10-3) Eb - (5.689x10-2) DHf -0.770 log P + 1.144 a - 0.671 m + 9.568 GLOB - (5.54x10-2) MR. n=19 r=0.917 SE=0.216 Fcal/Ftable=2.459 PRESS=0.469. The best model obtained was then used to design and predict the fungicides activity of new compounds derived from 1,2,4-thiadiazoline. Keywords: QSAR, QSPR, fungicide, molecular structure, 1,2,4-thiadiazoline
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Sarkar, Bikash Kumar. "DFT Based QSAR Studies of Phenyl Triazolinones of Protoporphyrinogen Oxidase Inhibitors." Asian Journal of Organic & Medicinal Chemistry 5, no. 4 (December 31, 2020): 307–11. http://dx.doi.org/10.14233/ajomc.2020.ajomc-p280.

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The quantitative structure activity relationships (QSARs) have been investigated on a series of substituted phenyl triazolinones having protoporphyrinogen oxidase (PPO) inhibition activities. The density functional theory (DFT) method is applied to calculate the quantum chemical descriptors. The derived QSAR model is based on three molecular descriptors namely highest occupied molecular orbital (HOMO) energy, electrophilic group frontier electron density (Fg E) and nucleus independent chemical shift (NICS). The best QSAR model has a square correlation coefficient r2 =0.886 and cross-validated square correlation coefficient q2 = 0.837.
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Rybińska-Fryca, Anna, Anita Sosnowska, and Tomasz Puzyn. "Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids." Materials 13, no. 11 (May 30, 2020): 2500. http://dx.doi.org/10.3390/ma13112500.

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The process of encoding the structure of chemicals by molecular descriptors is a crucial step in quantitative structure-activity/property relationships (QSAR/QSPR) modeling. Since ionic liquids (ILs) are disconnected structures, various ways of representing their structure are used in the QSAR studies: the models can be based on descriptors either derived for particular ions or for the whole ionic pair. We have examined the influence of the type of IL representation (separate ions vs. ionic pairs) on the model’s quality, the process of the automated descriptors selection and reliability of the applicability domain (AD) assessment. The result of the benchmark study showed that a less precise description of ionic liquid, based on the 2D descriptors calculated for ionic pairs, is sufficient to develop a reliable QSAR/QSPR model with the highest accuracy in terms of calibration as well as validation. Moreover, the process of a descriptors’ selection is more effective when the possible number of variables can be decreased at the beginning of model development. Additionally, 2D descriptors usually demand less effort in mechanistic interpretation and are more convenient for virtual screening studies.
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Pokle, Maithili S., Rashmi D. Singh, and Madhura P. Vaidya. "2D QSAR MODEL BASED ON 1,2-DISUBSTITUTED BENZIMIDAZOLES IMPDH INHIBITORS." Indian Drugs 59, no. 04 (June 1, 2022): 18–23. http://dx.doi.org/10.53879/id.59.04.13117.

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Quantitative structure activity relationship (QSAR) analysis of 1, 2-disubstituted benzimidazoles IMPDH inhibitors was studied for their antibacterial activity. The 2D QSAR model was developed using molecular suite (VLife MDS 4.3.1) on a set of 38 molecules. Multiple Linear Regression (MLR) was implemented for building a robust 2D QSAR model with various variable selection methods. The generated QSAR model emphasized that electronic, spatial, lipophilic and structural parameters play an important role in binding of benzimidazole derivatives to the receptor and thus in turn facilitates the further optimization of novel IMPDH inhibitors before synthesizing.
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Bu, Qingwei, Qingshan Li, Yun Liu, and Chun Cai. "Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable?" Journal of Chemistry 2021 (October 31, 2021): 1–8. http://dx.doi.org/10.1155/2021/5563066.

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Assessing the ecotoxicity of pharmaceuticals is of urgent need due to the recognition of their possible adverse effects on nontarget organisms in the aquatic environment. The reality of ecotoxicity data scarcity promotes the development and application of quantitative structure activity relationship (QSAR) models. In the present study, we aimed to clarify whether a QSAR model of ecotoxicity specifically for pharmaceuticals is needed considering that pharmaceuticals are a class of chemicals with complex structures, multiple functional groups, and reactive properties. To this end, we conducted a performance comparison of two previously developed and validated QSAR models specifically for pharmaceuticals with the commonly used narcosis toxicity prediction model, i.e., Ecological Structure Activity Relationship (ECOSAR), using a subset of pharmaceuticals produced in China that had not been included in the training datasets of QSAR models under consideration. A variety of statistical measures demonstrated that the pharmaceutical specific model outperformed ECOSAR, indicating the necessity of developing a specific QSAR model of ecotoxicity for the active pharmaceutical contaminants. ECOSAR, which was generally used to predict the baseline or the minimum toxicity of a compound, generally underestimated the ecotoxicity of the analyzed pharmaceuticals. This could possibly be because some pharmaceuticals can react through specific modes of action. Nonetheless, it should be noted that 95% prediction intervals spread over approximately four orders of magnitude for both tested QSAR models specifically for pharmaceuticals.
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LIAO, SI YAN, LI QIAN, JIN CAN CHEN, YONG SHEN, and KANG CHENG ZHENG. "2D/3D-QSAR STUDY ON ANALOGUES OF 2-METHOXYESTRADIOL WITH ANTICANCER ACTIVITY." Journal of Theoretical and Computational Chemistry 07, no. 02 (April 2008): 287–301. http://dx.doi.org/10.1142/s0219633608003745.

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Two-dimensional (2D) and three-dimensional (3D) quantitative structure–activity relationships (QSARs) of 23 analogs of 2-Methoxyestradiol with anticancer activity (expressed as p GI50) against MCF-7 human breast cancer cells have been studied by using a combined method of the DFT, MM2 and statistics for 2D, as well as the comparative molecular field analysis (CoMFA) for 3D. The established 2D-QSAR model in training set shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient [Formula: see text] and the square of the cross-validation coefficient (q2= 0.779). The same model was further applied to predict p GI50values of the four compounds in the test set, and the resulting [Formula: see text] being as high as 0.827, further confirms that this 2D-QSAR model has high predictive ability for this kind of compound. The 3D-QSAR model also shows good correlative and predictive capabilities in terms of R2(0.927) and q2(0.786) obtained from CoMFA model. The results that 2D- and 3D-QSAR analyses accord with each other, suggest that the electrostatic interaction plays a decisive role in determining the anticancer activity of the studied compounds, and that increasing the negative charge of substituent R2and the positive charge of substituents linking to C17as well as decreasing the size of substituent R1are advantageous to improving the cytotoxicity. Such results can offer some useful theoretical references for directing the molecular design and understanding the action mechanism of this kind of compound with anticancer activity.
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11

Zhao, Manman, Lin Wang, Linfeng Zheng, Mengying Zhang, Chun Qiu, Yuhui Zhang, Dongshu Du, and Bing Niu. "2D-QSAR and 3D-QSAR Analyses for EGFR Inhibitors." BioMed Research International 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4649191.

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Epidermal growth factor receptor (EGFR) is an important target for cancer therapy. In this study, EGFR inhibitors were investigated to build a two-dimensional quantitative structure-activity relationship (2D-QSAR) model and a three-dimensional quantitative structure-activity relationship (3D-QSAR) model. In the 2D-QSAR model, the support vector machine (SVM) classifier combined with the feature selection method was applied to predict whether a compound was an EGFR inhibitor. As a result, the prediction accuracy of the 2D-QSAR model was 98.99% by using tenfold cross-validation test and 97.67% by using independent set test. Then, in the 3D-QSAR model, the model with q2=0.565 (cross-validated correlation coefficient) and r2=0.888 (non-cross-validated correlation coefficient) was built to predict the activity of EGFR inhibitors. The mean absolute error (MAE) of the training set and test set was 0.308 log units and 0.526 log units, respectively. In addition, molecular docking was also employed to investigate the interaction between EGFR inhibitors and EGFR.
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12

Papa, Ester, Alessandro Sangion, Olivier Taboureau, and Paola Gramatica. "Quantitative Prediction of Rat Hepatotoxicity by Molecular Structure." International Journal of Quantitative Structure-Property Relationships 3, no. 2 (July 2018): 49–60. http://dx.doi.org/10.4018/ijqspr.2018070104.

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In this article, Quantitative Structure Activity Relationships (QSAR) were generated to link the structure of over 120 heterogeneous drugs to rat hepatotoxicity. Existing studies, performed on the same data set, could not highlight relevant structure-activity relationships, and suggested models for the prediction of hepatotoxicity based on genomic data. Binary activity responses were used for the development of classification QSARs using theoretical molecular descriptors calculated with the software PaDEL-Descriptor. A statistically powerful QSAR based on six descriptors was generated by using k-Nearest Neighbour (k-NN) method and by applying the Genetic Algorithm (GA) as variable selection procedure. The new k-NN QSAR outperforms published models by providing better accuracy and less false negatives. This model is a valid alternative to approaches based on genomic descriptors, which cannot be used in virtual screening of new compounds (pre- or post-synthesis) without experimental data.
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13

Vries, D., B. A. Wols, and P. de Voogt. "Removal efficiency calculated beforehand: QSAR enabled predictions for nanofiltration and advanced oxidation." Water Supply 13, no. 6 (September 13, 2013): 1425–36. http://dx.doi.org/10.2166/ws.2013.109.

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The efficiency of water treatment systems in removing emerging (chemical) substances is often unknown. Consequently, the prediction of the removal of contaminants in the treatment and supply chain of drinking water is of great interest. By collecting and processing existing chemical properties of contaminants, QSARs (quantitative structure-activity relationships) for typical removal parameters can be constructed. Depending on the definition of the predicted endpoint, QSARs are (1) embedded in a process model suite, where they serve to predict a model parameter and the total, hybrid model predicts a removal rate or (2) used to directly predict, e.g., the removal rate, or a rejection coefficient for membrane systems. The different types of resulting prediction models, ranging from mechanistic (causal) to empirical (data-based), allow for hypothesis testing of current physico-chemical mechanisms and interactions between the contaminant, the type of water and the materials or energy (e.g. UV light) of the removal barrier. Two case studies illustrate this viewpoint and also pinpoint that, firstly, QSAR development, validation and residual analysis stress the linkage between the QSAR endpoints and process model predictions, and secondly, they lay bare the need to share data, algorithms and models.
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14

Tosato, Maria Livia, Claudio Chiorboli, Lennart Eriksson, Jorgen Jonsson, Silvia Marchini, Laura Passerini, Anna Pino, and Luigi Viganó. "Quantitative Structure—Activity Relationships (QSARs): An Integrated Multivariate Approach for Risk Assessment Studies." Journal of the American College of Toxicology 9, no. 6 (November 1990): 629–38. http://dx.doi.org/10.3109/10915819009078768.

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The conditions and methods for constructing reliable QSARs are revised in relation to each component of a QSAR study: the selection of a training set out of a QSAR compatible series, the collection of data pertinent to the descriptors matrix (X) and to the effects matrix (Y), the analysis of data to connect X to Y by a regression model, and the validation of the model. In discussing these conditions, attention is given to the constraints that arise from the theoretical foundation of QSARs as analogy models of local validity and to the complexity and limited knowledge about the mechanisms of action. Hence, emphasis is placed on the need and importance to adopt multivariate methods for dealing with (1) the characterization of the structures, (2) the selection of a representative set of training compounds, and (3) analysis of the data. It is finally shown that the same integrated multivariate approach applies to the modeling of biological activities and other properties—chemical and biological—as well as to the modeling of correlations between batteries of data. The role of QSAR in risk assessment is addressed in the second part of the article. The framework of a strategy for an efficient screening assessment of toxic substances through the modeling of their exposure and toxicity-related properties is outlined. Applications of the strategy are reported that deal with two series of compounds. Examples of toxicity and persistency models are illustrated.
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15

Gombar, Vijay K., Harold H. Borgstedt, Kurt Enslein, Jeffrey B. Hart, and Benjamin W. Blake. "A QSAR Model of Teratogenesis." Quantitative Structure-Activity Relationships 10, no. 4 (1991): 306–32. http://dx.doi.org/10.1002/qsar.19910100404.

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16

Raevsky, Oleg, Alexei Sapegin, and Nikolai Zefirov. "The QSAR Discriminant-Regression Model." QSAR & Combinatorial Science 13, no. 4 (1994): 412–18. http://dx.doi.org/10.1002/qsar.19940130406.

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17

Kuz’min, Victor E., Anatoly G. Artemenko, Nikolay A. Kovdienko, Igor V. Tetko, and David J. Livingstone. "Lattice Model for QSAR Studies." Journal of Molecular Modeling 6, no. 7-8 (August 2000): 517–26. http://dx.doi.org/10.1007/s0089400060517.

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18

NAZİB ALİAS, Ahmad, and Zubainun MOHAMED ZABİDİ. "QSAR Studies on Nitrobenzene Derivatives using Hyperpolarizability and Conductor like Screening model as Molecular Descriptors." Journal of the Turkish Chemical Society Section A: Chemistry 9, no. 3 (August 31, 2022): 953–68. http://dx.doi.org/10.18596/jotcsa.1083840.

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Quantitative structure-activity relationship (QSAR) models were useful in understanding how chemical structure relates to the toxicology of chemicals. In the present study, we report quantum molecular descriptors using conductor like screening model (COs) area, the linear polarizability, first and second order hyperpolarizability for modelling the toxicology of the nitro substituent on the benzene ring. All the molecular descriptors were performed using semi-empirical PM6 approaches. The QSAR model was developed using stepwise multiple linear regression. We found that the stable QSAR modelling of toxicology benzene derivatives used second order hyper-polarizability and COs area, which satisfied the statistical measures. The second order hyperpolarizability shows the best QSAR model. We also discovered that the nitrobenzene derivative’s substitutional functional group has a significant effect on the quantum molecular descriptors, which reflect the QSAR model.
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T. Stanton, David. "QSAR and QSPR Model Interpretation Using Partial Least Squares (PLS) Analysis." Current Computer Aided-Drug Design 8, no. 2 (April 1, 2012): 107–27. http://dx.doi.org/10.2174/157340912800492357.

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20

Song, Xiaoying, Gaoya Wen, and Li Chai. "Graph signal processing based nonlinear QSAR/QSPR model learning for compounds." Biomedical Signal Processing and Control 91 (May 2024): 106011. http://dx.doi.org/10.1016/j.bspc.2024.106011.

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21

Mudasir, Mudasir, Yari Mukti Wibowo, and Harno Dwi Pranowo. "Design of New Potent Insecticides of Organophosphate Derivatives Based on QSAR Analysis." Indonesian Journal of Chemistry 13, no. 1 (May 6, 2013): 86–93. http://dx.doi.org/10.22146/ijc.21331.

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Design of new potent insecticide compounds of organophosphate derivatives based on QSAR (Quantitative Structure-Activity Relationship) analytical model has been conducted. Organophosphate derivative compounds and their activities were obtained from the literature. Computational modeling of the structure of organophosphate derivative compounds and calculation of their QSAR descriptors have been done by AM1 (Austin Model 1) method. The best QSAR model was selected from the QSAR models that used only electronic descriptors and from those using both electronic and molecular descriptors. The best QSAR model obtained was:Log LD50 = 50.872 - 66.457 qC1 - 65.735 qC6 + 83.115 qO7 (n = 30, r = 0.876, adjusted r2 = 0.741, Fcal/Ftab = 9.636, PRESS = 2.414 x 10-6)The best QSAR model was then used to design in silico new compounds of insecticide of organophosphate derivatives with better activity as compared to the existing synthesized organophosphate derivatives. So far, the most potent insecticide of organophosphate compound that has been successfully synthesized had log LD50 of -5.20, while the new designed compound based on the best QSAR model, i.e.: 4-(diethoxy phosphoryloxy) benzene sulfonic acid, had log LD50 prediction of -7.29. Therefore, the new designed insecticide compound is suggested to be synthesized and tested for its activity in laboratory for further verification.
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Bertato, Linda, Nicola Chirico, and Ester Papa. "QSAR Models for the Prediction of Dietary Biomagnification Factor in Fish." Toxics 11, no. 3 (February 23, 2023): 209. http://dx.doi.org/10.3390/toxics11030209.

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Xenobiotics released in the environment can be taken up by aquatic and terrestrial organisms and can accumulate at higher concentrations through the trophic chain. Bioaccumulation is therefore one of the PBT properties that authorities require to assess for the evaluation of the risks that chemicals may pose to humans and the environment. The use of an integrated testing strategy (ITS) and the use of multiple sources of information are strongly encouraged by authorities in order to maximize the information available and reduce testing costs. Moreover, considering the increasing demand for development and the application of new approaches and alternatives to animal testing, the development of in silico cost-effective tools such as QSAR models becomes increasingly important. In this study, a large and curated literature database of fish laboratory-based values of dietary biomagnification factor (BMF) was used to create externally validated QSARs. The quality categories (high, medium, low) available in the database were used to extract reliable data to train and validate the models, and to further address the uncertainty in low-quality data. This procedure was useful for highlighting problematic compounds for which additional experimental effort would be required, such as siloxanes, highly brominated and chlorinated compounds. Two models were suggested as final outputs in this study, one based on good-quality data and the other developed on a larger dataset of consistent Log BMFL values, which included lower-quality data. The models had similar predictive ability; however, the second model had a larger applicability domain. These QSARs were based on simple MLR equations that could easily be applied for the predictions of dietary BMFL in fish, and support bioaccumulation assessment procedures at the regulatory level. To ease the application and dissemination of these QSARs, they were included with technical documentation (as QMRF Reports) in the QSAR-ME Profiler software for QSAR predictions available online.
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Zhang, Jiaming, Qinqin Liu, Haoxia Zhao, Guiyu Li, Yunpeng Yi, and Ruofeng Shang. "Design and Synthesis of Pleuromutilin Derivatives as Antibacterial Agents Using Quantitative Structure–Activity Relationship Model." International Journal of Molecular Sciences 25, no. 4 (February 13, 2024): 2256. http://dx.doi.org/10.3390/ijms25042256.

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The quantitative structure–activity relationship (QSAR) is one of the most popular methods for the virtual screening of new drug leads and optimization. Herein, we collected a dataset of 955 MIC values of pleuromutilin derivatives to construct a 2D-QSAR model with an accuracy of 80% and a 3D-QSAR model with a non-cross-validated correlation coefficient (r2) of 0.9836 and a cross-validated correlation coefficient (q2) of 0.7986. Based on the obtained QSAR models, we designed and synthesized pleuromutilin compounds 1 and 2 with thiol-functionalized side chains. Compound 1 displayed the highest antimicrobial activity against both Staphylococcus aureus ATCC 29213 (S. aureus) and Methicillin-resistant Staphylococcus aureus (MRSA), with minimum inhibitory concentrations (MICs) < 0.0625 μg/mL. These experimental results confirmed that the 2D and 3D-QSAR models displayed a high accuracy of the prediction function for the discovery of lead compounds from pleuromutilin derivatives.
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Gupta, Ashish, Virender Kumar, and Polamarasetty Aparoy. "Role of Topological, Electronic, Geometrical, Constitutional and Quantum Chemical Based Descriptors in QSAR: mPGES-1 as a Case Study." Current Topics in Medicinal Chemistry 18, no. 13 (October 4, 2018): 1075–90. http://dx.doi.org/10.2174/1568026618666180719164149.

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Quantitative Structure Activity Relationship (QSAR) is one of the widely used ligand based drug design strategies. Although a number of QSAR studies have been reported, debates over the limitations and accuracy of QSAR models are at large. In this review the applicability of various classes of molecular descriptors in QSAR has been explained. Protocol for QSAR model development and validation is presented. Here we discuss a case study on 7-Phenyl-imidazoquinolin-4(5H)-one derivatives as potent mPGES-1 inhibitors to identify crucial physicochemical properties responsible for mPGES-1 inhibition. The case study explains the methodology for QSAR analysis, validation of the developed models and role of diverse classes of molecular descriptors in defining the inhibitory activity of considered inhibitors. Various molecular descriptors derived from 2D/3D structure and quantum mechanics were considered in the study. Initially, QSAR models for the training set compounds were developed individually for each class of molecular descriptors. Further, a combined QSAR model was developed using the best descriptor from all the classes. The models obtained were further validated using an external test set. Combined QSAR model exhibited the best correlation (r = 0.80) between the predicted and experimental biological activities of test set compounds. The results of the QSAR analysis were further backed by docking studies. From the results of the case study it is evident that rather than a single class of molecular descriptors, a combination of molecular descriptors belonging to different classes significantly improves the QSAR predictions. The techniques and protocol discussed in the present work might be of significant importance while developing QSAR models of various drug targets.
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Rahmouni, Ali, Moufida Touhami, and Tahar Benaissa. "Fukui Indices as QSAR Model Descriptors." International Journal of Chemoinformatics and Chemical Engineering 6, no. 2 (July 2017): 31–44. http://dx.doi.org/10.4018/ijcce.2017070103.

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This article describes the Quantitative structure–activity relationship models of 1-[2-hydroxyethoxy-methyl]-6-(phenylthio) thymine inhibition of the human immunodeficiency virus (HIV-1) reverse transcriptase (RT) was developed using the multi linear regressions method. These studies were performed using 60 compounds with the help of quantum descriptors as Ionization Potential, Electron Affinity, Softness, global Electrophilicity index and Fukui functions. These indices are obtained at the DFT/B3LYP level of quantum calculation. The statistical quality of the QSAR models was assessed using statistical parameters R2. Good agreements between experimental and calculated log1/EC50 values of anti-HIV activity were obtained. Four QSAR models are presented and the best one use nine molecular quantum descriptors.
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Diyah, Nuzul Wahyuning, Dhea Ananda Ainurrizma, and Denayu Pebrianti. "Design of acyl salicylic acid derivates as COX-1 inhibitors using QSAR approach, molecular docking and QSPR analysis." Pharmacy Education 24, no. 3 (May 1, 2024): 88–94. http://dx.doi.org/10.46542/pe.2024.243.8894.

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Background: Acetylsalicylic acid (aspirin), widely used as an antiplatelet agent, is more likely to inhibit COX-1. Along with discovering the cardioprotective role of COX-1 in controlling platelet aggregation, it is important to develop a selective COX-1 inhibitor. Objective: This study aims to design acyl salicylic acid derivatives intended as COX-1 inhibitors. Method: Fourteen derivatives (AcS1-14) were subjected to a quantitative structure-activity relationship (QSAR) study, and 31 QSAR models were obtained using multiple linear regression (MLR) analysis. Molecular docking was performed on COX-1 (PDB. 1PTH) using the Molecular Orbital Environment (MOE) program ver2022.02, and QSPR analysis was conducted to ascertain the contribution of physicochemical descriptors to the free energy score (S) of ligand-receptor complexes. Results: The QSAR-Hansch model predicted hydrophobicity (LogP) and molecular energy (Etotal) and contributed to pain inhibitory action. All derivatives displayed higher in silico affinity than aspirin (S= -4.33±0.00 kcal/mol), and compound AcS7 afforded the highest (S= -5.32 kcal/mol). In QSPR, Etotal also revealed a positive contribution to the affinity. AcS1, AcS2, AcS5, AcS7, and AcS8 expressed higher drug-like properties than aspirin. Conclusion: Derivatives with optimum hydrophobicity and high energy would generate potent COX-1 inhibition. The five selected compounds were recommended to be developed as drug candidates for COX-1 inhibitors.
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Helda Wika Amini, Istiqomah Rahmawati, Rizki Fitria Darmayanti, and Boy Arief Fachri. "QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP STUDY OF ESTER-BASED FERULIC ACID DERIVATIVES AGAINST CERVICAL CANCER CELL (HELA)." Journal of Biobased Chemicals 1, no. 1 (June 5, 2020): 29–37. http://dx.doi.org/10.19184/jobc.v1i1.109.

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Quantitative structure-activity relationship (QSAR) has been studied for ferulic acid derivatives to determine the QSAR model able to predict anticancer. As the subject of this research was a set of experimentally calculated IC50 value data of 6 ferulic acid derivatives against cervical cancer cells (HELA). QSAR analysis was based on multilinear regression calculation on fitting subset using log (1/IC50) as the dependent variable, and dipole moment, partition coefficient in the n-octanol/water, and atomic net charges of the aromatic carbons as independent variables. The values of the descriptors were obtained from semiempirical PM3 quantum mechanic calculation. The relationship between log (1/IC50) and the descriptors was described by the result in the QSAR model. The QSAR model for ferulic acid derivatives against HELA cell lineswas developed with the statistical parameters of R=0.998; R2=0.999; SE=0.00857; and F=394. The calculated log (1/IC50) using QSAR Hansch Model for ferulic acid derivatives have excellent agreement with experimental data of Log (1/IC50).
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Rakhimbekova, Assima, Timur I. Madzhidov, Ramil I. Nugmanov, Timur R. Gimadiev, Igor I. Baskin, and Alexandre Varnek. "Comprehensive Analysis of Applicability Domains of QSPR Models for Chemical Reactions." International Journal of Molecular Sciences 21, no. 15 (August 3, 2020): 5542. http://dx.doi.org/10.3390/ijms21155542.

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Nowadays, the problem of the model’s applicability domain (AD) definition is an active research topic in chemoinformatics. Although many various AD definitions for the models predicting properties of molecules (Quantitative Structure-Activity/Property Relationship (QSAR/QSPR) models) were described in the literature, no one for chemical reactions (Quantitative Reaction-Property Relationships (QRPR)) has been reported to date. The point is that a chemical reaction is a much more complex object than an individual molecule, and its yield, thermodynamic and kinetic characteristics depend not only on the structures of reactants and products but also on experimental conditions. The QRPR models’ performance largely depends on the way that chemical transformation is encoded. In this study, various AD definition methods extensively used in QSAR/QSPR studies of individual molecules, as well as several novel approaches suggested in this work for reactions, were benchmarked on several reaction datasets. The ability to exclude wrong reaction types, increase coverage, improve the model performance and detect Y-outliers were tested. As a result, several “best” AD definitions for the QRPR models predicting reaction characteristics have been revealed and tested on a previously published external dataset with a clear AD definition problem.
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Zaki, Magdi E. A., Sami A. Al-Hussain, Vijay H. Masand, Siddhartha Akasapu, Sumit O. Bajaj, Nahed N. E. El-Sayed, Arabinda Ghosh, and Israa Lewaa. "Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis." Pharmaceuticals 14, no. 4 (April 13, 2021): 357. http://dx.doi.org/10.3390/ph14040357.

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Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure−Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA–MLR (Genetic Algorithm–Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole–indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.
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Xu, Yong-jin, and Hua Gao. "Dimension related distance and its application in QSAR/QSPR model error estimation." QSAR & Combinatorial Science 22, no. 4 (May 2003): 422–29. http://dx.doi.org/10.1002/qsar.200390032.

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Sharma, M. C., and D. V. Kohli. "DEVELOPMENT OF A ROBUST QSAR MODEL OF ANGIOTENSIN RECEPTOR REVEALS A K NEAREST NEIGHBOR APPLICABLE TO DIVERSE SCAFFOLDS." INDIAN DRUGS 54, no. 06 (June 28, 2017): 30–36. http://dx.doi.org/10.53879/id.54.06.10947.

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Quantitative structure–activity relationship (QSAR) studies were performed on quinazolinone analogues for prediction of antihypertensive activity. The best significant 2D-QSAR model having r2 = 0.8118 and pred_r2 = 0.7428 was developed by stepwise-partial least square method. k-nearest neighbor molecular field analysis was used to construct the best 3D-QSAR model, showing good correlative and predictive capabilities in terms of q2 = 0.7388 and pred_r2 = 0.6983. Results reveal that the 2D-QSAR studies signify positive contribution of SssOE index and SsCH3 count towards the biological activity. The results have showed that electronegative groups are necessary for activity and halogen, bulky, less bulky groups in quinazolinones nucleus enhanced the biological activity. The information rendered by 2D- and 3D-QSAR models may lead to a better understanding of structural requirements of substituted quinazolinones derivatives and also aid in designing novel potent antihypertensive molecules.
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Sharma, M. C. "A QSAR STUDY OF SUBSTITUTED PYRAZOLINE DERIVATIVES AS POTENTIAL ANTI-TUBERCULOSIS AGENTS." INDIAN DRUGS 54, no. 04 (April 28, 2017): 22–31. http://dx.doi.org/10.53879/id.54.04.10781.

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A quantitative structure–activity relationship (QSAR) of a series of substituted pyrazoline derivatives, in regard to their anti-tuberculosis activity, has been studied using the partial least square (PLS) analysis method. QSAR model development of 64 pyrazoline derivatives was carried out to predict anti-tubercular activity. Partial least square analysis was applied to derive QSAR models, which were further evaluated for statistical significance and predictive power by internal and external validation. The best QSAR model with good external and internal predictivity for the training and test set has shown cross validation (q2) and external validation (pred_r2) values of 0.7426 and 0.7903, respectively. Two-dimensional QSAR analyses of such pyrazoline derivatives provide important structural insights for designing potent antituberculosis drugs.
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Matusevičiūtė, Ramona, Eglė Ignatavičiūtė, Rokas Mickus, Sergio Bordel, Vytenis Arvydas Skeberdis, and Vytautas Raškevičius. "Evaluation of Cx43 Gap Junction Inhibitors Using a Quantitative Structure-Activity Relationship Model." Biomedicines 11, no. 7 (July 12, 2023): 1972. http://dx.doi.org/10.3390/biomedicines11071972.

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Gap junctions (GJs) made of connexin-43 (Cx43) are necessary for the conduction of electrical impulses in the heart. Modulation of Cx43 GJ activity may be beneficial in the treatment of cardiac arrhythmias and other dysfunctions. The search for novel GJ-modulating agents using molecular docking allows for the accurate prediction of binding affinities of ligands, which, unfortunately, often poorly correlate with their potencies. The objective of this study was to demonstrate that a Quantitative Structure-Activity Relationship (QSAR) model could be used for more precise identification of potent Cx43 GJ inhibitors. Using molecular docking, QSAR, and 3D-QSAR, we evaluated 16 known Cx43 GJ inhibitors, suggested the monocyclic monoterpene d-limonene as a putative Cx43 inhibitor, and tested it experimentally in HeLa cells expressing exogenous Cx43. The predicted concentrations required to produce 50% of the maximal effect (IC50) for each of these compounds were compared with those determined experimentally (pIC50 and eIC50, respectively). The pIC50ies of d-limonene and other Cx43 GJ inhibitors examined by our QSAR and 3D-QSAR models showed a good correlation with their eIC50ies (R = 0.88 and 0.90, respectively) in contrast to pIC50ies obtained from molecular docking (R = 0.78). However, molecular docking suggests that inhibitor potency may depend on their docking conformation on Cx43. Searching for new potent, selective, and specific inhibitors of GJ channels, we propose to perform the primary screening of new putative compounds using the QSAR model, followed by the validation of the most suitable candidates by patch-clamp techniques.
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Mishra, Durgesh Kumar, Ashutosh Singh, Sunil Mishra, Priti Singh, and Abhishek Singh. "PM3 Method based QSAR Study of the Derivatives of Thiadiazole and Quinoxaline for Antiepileptic Activity using Topological Descriptors." Asian Journal of Organic & Medicinal Chemistry 7, no. 1 (2022): 99–110. http://dx.doi.org/10.14233/ajomc.2022.ajomc-p370.

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QSAR study of the derivatives of thiadiazole and quinoxaline has been performed for the antiepileptic activity using the topological descriptors viz., molar refractivity, shape index (basic kappa, order 1), shape index (basic kappa, order 2), shape index (basic kappa, order 3), valence connectivity index (order 0, standard), valence connectivity index (order 1, standard) and valence connectivity index (order 2, standard). In the best QSAR model, the descriptors are molar refractivity, shape index (basic kappa, order 1), shape index (basic kappa, order 3) and valence connectivity index (order 0, standard). In this QSAR model, the regression coefficient is 0.872435 and cross-validation coefficient is 0.832189, which indicate that this QSAR model can be used to predict the antiepileptic activity of any compound belonging to this series. QSAR model developed using single descriptor shape index (basic kappa, order 1) or shape index (basic kappa, order 3) or valence connectivity index (order 2, standard) also has good predictive power.
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Chakraborty, Tanmoy, and Dulal C. Ghosh. "Correlation of the Drug Activities of Some Anti-Tubercular Chalcone Derivatives in Terms of the Quantum Mechanical Reactivity Descriptors." International Journal of Chemoinformatics and Chemical Engineering 1, no. 2 (July 2011): 53–65. http://dx.doi.org/10.4018/ijcce.2011070104.

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Under the QSPR/QSAR paradigm, a comparative study is made of the known drug activity of as many as 15 anti-tubercular drugs vis-à-vis the computed quantum mechanical global reactivity descriptors like global hardness, global softness and global electrophilicity index. The comparative study reveals that the experimentally determined activity of drug molecules, including its variation with side substitution on the parent moiety, correlate nicely with the theoretical descriptors. The global electrophilicity index of a molecule may be useful in predicting the mechanism of the drug receptor interaction. In addition, the authors predicted the QSAR models to correlate the antitubercular activities with quantum mechanical descriptors like global hardness, electronegativity, global softness, and global electrophilicity index. The multilinear model using all four global descriptors computed through PM3 method, effectively predicts the antitubercular activities for a series of chalcone derivatives. The high value of R2 (0.961) supports the validity of that particular model. A nice correlation between the predicted and experimental activities validates the effort.
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Naik, Pradeep Kumar, Afroz Alam, Ashutosh Malhotra, and Owasis Rizvi. "Molecular Modeling and Structure-Activity Relationship of Podophyllotoxin and Its Congeners." Journal of Biomolecular Screening 15, no. 5 (May 10, 2010): 528–40. http://dx.doi.org/10.1177/1087057110368994.

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A quantitative structure-activity relationship (QSAR) model has been developed between cytotoxic activity and structural properties by considering a data set of 119 podophyllotoxin analogs based on 2D and 3D structural descriptors. A systematic stepwise searching approach of zero tests, a missing value test, a simple correlation test, a multicollinearity test, and a genetic algorithm method of variable selection was used to generate the model. A statistically significant model ( r train2 = 0.906; q cv2 = 0.893) was obtained with the molecular descriptors. The robustness of the QSAR model was characterized by the values of the internal leave-one-out cross-validated regression coefficient ( q cv2) for the training set and r test2 for the test set. The overall root mean square error (RMSE) between the experimental and predicted pIC50 value was 0.265 and r test2 = 0.824, revealing good predictability of the QSAR model. For an external data set of 16 podophyllotoxin analogs, the QSAR model was able to predict the tubulin polymerization inhibition and mechanistically cytotoxic activity with an RMSE value of 0.295 in comparison to experimental values. The QSAR model developed in this study shall aid further design of novel potent podophyllotoxin derivatives.
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Mishra, Puja, Sumit Nandi, Ankit Chatterjee, Tridib Nayek, Souvik Basak, Kumar Halder, and Arup Mukherjee. "Development of 2D and 3D QSAR models of pyrazole derivatives as acetylcholine esterase inhibitors." Journal of the Serbian Chemical Society, no. 00 (2024): 39. http://dx.doi.org/10.2298/jsc230221039m.

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The drugs that are most useful in all stages of Alzheimer?s disease (AD) are acetylcholinesterase (AChE) inhibitors. The objectives of this work are to generate various QSAR models and to select robust predictive models from corresponding models. Studies were then focused on finding a range of pyrazole-like AChE inhibitors by 2D and 3D QSAR analysis. Genetic algorithm-based multiple linear regression (GA-MLR) provided the statistically robust 2D-QSAR model that depicted the significance of molecular volume and number of multiple bonds along with the presence/absence of specific atom-centred fragments and topological distance between 2D pharmacophoric features. Furthermore, these results were correlated well with the electrostatic and steric contour maps retrieved from the 3D-QSAR (i.e., alignment-dependent molecular field analysis). The 2D QSAR analysis developed a highly statistical and reliable model which was compared with the mechanistic interpretation of 3D structures and their electrostatic and steric field contributions leading to a predictive 3D QSAR model. The molecule-protein interactions elicited by molecular docking corroborated with the field interactions as revealed by 2D-QSAR. Thus, the developed computational models and simulation analyses in the current work provide valuable information for the future design of pyrazole and spiropyrazoline analogs as potent AChE inhibitors.
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Asgaonkar, K. D., S. M. Patil, T. S. Chitre, S. D. Wani, and M. T. Singh. "QSAR tool for optimization of nitrobenzamide pharmacophore for antitubercular activity." Bulletin of the Karaganda University. "Chemistry" series 105, no. 1 (March 30, 2022): 60–68. http://dx.doi.org/10.31489/2022ch1/60-68.

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Tuberculosis (TB) is a leading cause of death worldwide from a single infectious agent, Mycobacterium tuberculosis (MTB), especially due to the development of resistant strains and its co-infections in HIV. Quantitative-structure activity relationship (QSAR) studies aid rapid drug discovery. In this work, 2D and 3D QSAR studies were carried out on a series of nitrobenzamide derivatives to design newer analogues for antitubercular activity. 2D QSAR was performed using MLR on a data set showing antitubercular activity. The 3D-QSAR studies were performed by kNN–MFA using simulated annealing variable selection method. Alignment of given set of molecules was carried out by the template-based alignment method and then was used to build the 3D-QSAR model. Robustness and predictive ability of the models were evaluated by using various traditional validating parameters. Different physiochemical, alignment-based, topological, electrostatic, and steric descriptors were generated, which indicated the key structural requirements for optimizing the pharmacophore for better antitubercular activity. For 2D QSAR, the best statistical model was generated using SA-MLR method (r2 = 0.892, q2 = 0.819) while 3D QSAR model was derived using the SA KNN method (q2 = 0.722). The positively contributing descriptors can be incorporated to design new chemical entities for future study.
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Edache, Emmanuel Israel, Adamu Uzairu, Paul Andrew Mamza, and Gideon Adamu Shallangwa. "A comparative QSAR analysis, 3D-QSAR, molecular docking and molecular design of iminoguanidine-based inhibitors of HemO: A rational approach to antibacterial drug design." Journal of Drugs and Pharmaceutical Science 4, no. 3 (August 30, 2020): 21–36. http://dx.doi.org/10.31248/jdps2020.036.

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QSAR models of 25 iminoguanidine derivatives with inhibitory HemO were developed. The QSAR model was built by using DFT-MLR and the best QSAR model has R2, Q2 values of 0.6569 and 0.5493 for cross-validated and non-cross-validated. The predictive ability of QSAR model was further validated by a test set of 7 compounds, giving R2pred value of 0.7123. 3D-QSAR and docking studies were used to find the actual conformations of chemicals in active site of HemO, as well as the binding mode pattern to the binding site in HemO enzyme. Molecular dynamics and simulations study revealed that A-chain of HemO protein was stable at and above 100ps with respect to temperature (at and above 298 K), electrostatic (at and above 57500 kJ/mol), kinetic energy (at and above 12200 kJ/mol) and total energy (at and above 30500 kJ/mol). The information provided by QSAR, 3D-QSAR and molecular docking may provide a better understanding of the structural requirements of iminoguanidine-based inhibitors of HemO and help to design potential antibacterial molecules. Four (4) new compounds (4A, 5A, 6A, and 7A) with high predicted antibacterial activities have been theoretically designed and they are expected to be confirmed experimentally.
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Sharma, M. C. "QSAR APPROACH TO THE STUDY OF THE EGFR TYROSINE KINASE INHIBITORS: THIAZOLYL-PYRAZOLINE DERIVATIVES." INDIAN DRUGS 54, no. 03 (March 28, 2017): 5–12. http://dx.doi.org/10.53879/id.54.03.10739.

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A 2D-QSAR model was developed to identify key properties of thiazolyl-pyrazoline derivatives analogs involved in the inhibition of the EGFR protein tyrosine kinase. Variable selection was performed by multiple linear regression method using Build QSAR Vlife Science MDS software to develop QSAR model. The best QSAR model consists of four descriptors SddsN (nitro) count, T_2_Cl_1, SsBrE-index and T_O_F_1 descriptors, and has correlation coefficient of 0.8069 and a cross-validated squared correlation coefficient of 0.7332. All the compounds produce positive scores, which suggest that the compounds may have good kinase inhibitory profile. The developed models may be useful to predict EGFR inhibition activity for the newly synthesized thiazolyl-pyrazoline analogues.
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Raza, Zahid. "Leap Zagreb Connection Numbers for Some Networks Models." Indonesian Journal of Chemistry 20, no. 6 (July 16, 2020): 1407. http://dx.doi.org/10.22146/ijc.53393.

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The main object of this study is to determine the exact values of the topological indices which play a vital role in studying chemical information, structure properties like QSAR and QSPR. The first Zagreb index and second Zagreb index are among the most studied topological indices. We now consider analogous graph invariants, based on the second degrees of vertices, called leap Zagreb indices. We compute these indices for Tickysim SpiNNaker model, cyclic octahedral structure, Aztec diamond and extended Aztec diamond.
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Završnik, Davorka, Samija Muratović, and Selma Špirtović. "QSAR and QSPR study of derivatives 4-arylaminocoumarin." Bosnian Journal of Basic Medical Sciences 3, no. 3 (August 20, 2003): 59–63. http://dx.doi.org/10.17305/bjbms.2003.3531.

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Coumarin and its derivatives are reactive compounds, suitable for many syntheses. They are used as anticoagulants, antibacterial, animistic compounds. The interest in coumarins has increased because it was found that they reduce the HIV virus activity. The synthesis of 4-arylaminocoumarin derivatives from 4-hydroxycoumarin, has been carried out, and their antimycotic effects were tested. In the QSAR (quantitative structure-activity relationship) QSPR (quantitativestructure-property-activity relationship) study we have used physicochemical properties and topological indices (Balaban index J(G), Wiener index W(G), information-theoretical index I(G), and valence connectivity index (G), to predict bioactivity of the newly synthesized coumarin compounds. By using methods of molecular modelling, the relationships between structure, properties and activity of coumarin compounds have been investigated. The best QSPR models were obtained using valence connectivity index or combination indices. According Rekker's method the best correlation of calculated values log P, has been obtained with the model based on the inhibition zone (I) 4-arylaminocoumarin derivatives expressed in mm. The results obtained in this study enable further synthesis of new coumarin derivatives and predict their biological activity and properties.
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Faramarzi, Zohreh, Fatemeh Abbasitabar, Jalali Jahromi, and Maziar Noei. "New structure-based models for the prediction of normal boiling point temperature of ternary azeotropes." Journal of the Serbian Chemical Society 86, no. 7-8 (2021): 685–98. http://dx.doi.org/10.2298/jsc210218035f.

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Recently, development of the QSPR models for mixtures has received much attention. The QSPR modelling of mixtures requires the use of the appropriate mixture descriptors. In this study, 12 mathematical equations were considered to compute mixture descriptors from the individual components for the prediction of normal boiling points of 78 ternary azeotropic mixtures. Multiple linear regression (MLR) was employed to build all QSPR models. Memorized_ ACO algorithm was employed for subset variable selection. An ensemble model was also constructed using averaging strategy to improve the predictability of the final QSAR model. The models have been validated by a test set comprised of 24 ternary azeotropes and by different statistical tests. The resulted ensemble QSPR model had R2 training, R2 test and q2 of 0.97, 0.95, and 0.96, respectively. The mean absolute error (MAE), as a good indicator of model performance, were found to be 3.06 and 3.52 for training and testing sets, respectively.
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Didziapetris, Remigijus, Kiril Lanevskij, and Pranas Japertas. "Trainable QSAR model of Ames genotoxicity." Toxicology Letters 180 (October 2008): S152—S153. http://dx.doi.org/10.1016/j.toxlet.2008.06.335.

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Nirmalakhandan, Nagamany N., and Richard E. Speece. "QSAR model for predicting Henry's constant." Environmental Science & Technology 22, no. 11 (November 1988): 1349–57. http://dx.doi.org/10.1021/es00176a016.

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Amin, Sk Abdul, Nilanjan Adhikari, Tarun Jha, and Shovanlal Gayen. "Exploring structural requirements of unconventional Knoevenagel-type indole derivatives as anticancer agents through comparative QSAR modeling approaches." Canadian Journal of Chemistry 94, no. 7 (July 2016): 637–44. http://dx.doi.org/10.1139/cjc-2016-0050.

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An indole ring system is considered as a versatile scaffold in the pharmaceutical field. In this article, comparative QSAR modeling (2D-QSAR, 3D-QSAR; kNN-MFA and CoMSIA) was performed on some Knoevenagel-type cytotoxic indole derivatives to understand the structural requirements for the cytotoxic property of these compounds. The 2D-QSAR model was statistically significant and imparted high predictive ability (nTrain = 30; R = 0.917; [Formula: see text] = 0.801; [Formula: see text] = 0.757; Q2 = 0.722; nTest = 9; [Formula: see text] = 0.799). A statistically significant 3D-QSAR kNN-MFA model (both with stepwise forward and simulated annealing model selection method) as well as a 3D-QSAR CoMSIA model was developed to identify the key chemical features associated with enhancing the cytotoxic activities of these indoles. The results suggest that the presence of bulky group in R position can cause better cytotoxic activities. Consequently, substitution with cyano group at X portion and cyano/ester/keto/sulphonyl features at Y position is favourable for the cytotoxicity. However, hydrophobic features in R′ region are unfavourable for the biological activity. The chemical and structural features identified from the study may provide important avenues to modulate the structure of these indoles to a desirable biological end point.
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Pandit, Bibhas, Yogesh Vaishnav, Sanjib Bahadur, and Trilochan Satapathy. "2D & 3D-QSAR Studies on a Series of Quinoline-Amino-piperidine Derivatives as Potent Mycobacterium DNA-Gyrase-B Inhibitors." International Journal of Pharmaceutical Sciences and Nanotechnology(IJPSN) 16, no. 3 (May 31, 2023): 6512–21. http://dx.doi.org/10.37285/ijpsn.2023.16.3.5.

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Introduction: Mycobacterium tuberculosis is a familiar infectious bacillus that causes tuberculosis, which primarily affects the lungs and the spinal cord. To combat the growing difficulties in treating MTB, it is necessary to create safe medications with novel mechanisms of action. Objective: To design and develop some novel quinolone-amino piperidine derivatives with potent mycobacterium DNAgyraseB inhibitory using the QSAR technique. Methods: Multiple linear regression (MLR), partial least squares (PLS), and k-nearest neighbour molecular field analysis ((kNN-MFA) were utilised in the development of 2D and 3D-QSAR models, respectively; these models were then validated. Results: The recently developed 2D-QSAR model can explain 85.07% (r2 = 0.8507) of the total variance incorporated into the training set. In addition, the model has an internal prediction capacity (q2) of 77.65% and an external prediction capacity (pred r2) of 83.64%, respectively. The F test confirms that the likelihood of the model failing is extremely low. The 3D-QSAR model explains the values of k (2), q2 = 0.5707, pred r2 = 0.7843, q2 se = 0.3167, and pred r2 se = 0.3111. This demonstrates that the QSAR equation obtained in that way is statistically significant and that the model has a predictive capacity of 78.43%. Conclusion: The robustness of the developed 2D or 3D-QSAR models provides the necessary information and is expected to provide an excellent option for drug design.
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Emdeniz. "PENGGUNAAN FRONTIER ORBITAL MOLEKUL SEBAGAI DESKRIPTOR PADA ANALISIS HUBUNGAN KUANTITATIF STRUKTUR AKTIVITAS (HKSA) TOKSIK SENYAWA KHLOROFENOL." Jurnal Riset Kimia 5, no. 2 (March 15, 2012): 116. http://dx.doi.org/10.25077/jrk.v5i2.211.

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Quantitative structure-activity relationship (QSAR) methods have been applied to prediction of the toxicity of certain chemical compound. In this research a QSAR descriptor used frontier molecular orbital (LUMO energy (EL), HOMO energy (EH), and band gap (ΔE) and its derivatives were obtained from density functional theory (DFT) (chemical hardness (η) chemical potential (μ) or absolute electronegativity (χ) and global electrophicility indeks (ω)). Frontier molecular orbital (EL and EH) was calculated by ab initio quantum methods. This research found the correlation between the experimental ecotoxicological data of chlorophenols and toxicity prediction were calculated based on the best QSAR equation model of all equation model which have been studied. The best QSAR equation model using parameter LUMO energy (EL), and global electrophilicity index (ω) as descriptor on QSAR toxic of chlorophenol compounds against Bacilus sp TL81 is - log IC50 = 11,022 - 1,767 EL - 5,687 ω, and it has the coefficient of determination (R2) = 0,581 and standard deviation (SD) = 0,6111.
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Terzioglu, Nalan, and Hans-Dieter Höltje. "Receptor-Based 3D QSAR Analysis of Serotonin 5-HT1D Receptor Agonists." Collection of Czechoslovak Chemical Communications 70, no. 9 (2005): 1482–92. http://dx.doi.org/10.1135/cccc20051482.

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A three-dimensional quantitative structure-activity relationship study (3D QSAR) has been successfully applied to explain the binding affinities for the serotonin 5-HT1D receptor of a triptan series. The paper describes the development of a receptor-based 3D QSAR model of some known agonists and recently developed triptans on the 5-HT1D serotonergic receptor, showing a significant correlation between predicted and experimentally measured binding affinity (pIC50). The pIC50 values of these agonists are in the range from 5.40 to 9.50. The ligand alignment obtained from dynamic simulations was taken as basis for a 3D QSAR analysis applying the GRID/GOLPE program. 3D QSAR analysis of the ligands resulted in a model of high quality (r2 = 0.9895, q2LOO = 0.7854). This is an excellent result and proves both the validity of the proposed pharmacophore and the predictive quality of the 3D QSAR model for the triptan series of serotonin 5-HT1D receptor agonists.
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Nasution, Hasmalina, Nur Enizan, Nurlaili Nurlaili, and Jufrizal Syahri. "Design of Trolox Compounds as Antioxidant and Their Analysis Using Quantitative Structure Activity Relationship." Acta Chimica Asiana 3, no. 2 (October 18, 2020): 181. http://dx.doi.org/10.29303/aca.v3i2.40.

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Antioxidant compound can inhibit the oxidation of lipids and other biomolecules. The role of antioxidants is very important in neutralizing and destroying free radicals that can cause the damage to cells in the body. This research was carried out to design trolox derivate compounds as antioxidants using the QSAR method. The semi empirical AM1(Austin Model 1)method was used to generate the QSAR model. The statistical analysis result using multiple linier regression methods revealed thet antioxidant activity was influenced by the descriptors of qC1, qC4, qO7, qC13 and qO18. The QSAR equation model obtained was log IC50 = 0.821 + 7.067 (qC1) + 2.585 (qC4) + 4.812 (qO7) – 5.363 (qC13) – 0.887 (qO18) with the best predicted IC50 value was 4.699 µM. Keywords: Antioxidants, QSAR, semi empirical AM1, trolox
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