Journal articles: 'QSAR'

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Toropov, Andrey A., and Alla P. Toropova. "QSPR/QSAR: State-of-Art, Weirdness, the Future." Molecules 25, no. 6 (March 12, 2020): 1292. http://dx.doi.org/10.3390/molecules25061292.

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Ability of quantitative structure–property/activity relationships (QSPRs/QSARs) to serve for epistemological processes in natural sciences is discussed. Some weirdness of QSPR/QSAR state-of-art is listed. There are some contradictions in the research results in this area. Sometimes, these should be classified as paradoxes or weirdness. These points are often ignored. Here, these are listed and briefly commented. In addition, hypotheses on the future evolution of the QSPR/QSAR theory and practice are suggested. In particular, the possibility of extending of the QSPR/QSAR problematic by searching for the “statistical similarity” of different endpoints is suggested and illustrated by an example for relatively “distanced each from other” endpoints, namely (i) mutagenicity, (ii) anticancer activity, and (iii) blood–brain barrier.

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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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Costa, Paulo C. S., Joel S. Evangelista, Igor Leal, and Paulo C. M. L. Miranda. "Chemical Graph Theory for Property Modeling in QSAR and QSPR—Charming QSAR & QSPR." Mathematics 9, no. 1 (December 29, 2020): 60. http://dx.doi.org/10.3390/math9010060.

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Quantitative structure-activity relationship (QSAR) and Quantitative structure-property relationship (QSPR) are mathematical models for the prediction of the chemical, physical or biological properties of chemical compounds. Usually, they are based on structural (grounded on fragment contribution) or calculated (centered on QSAR three-dimensional (QSAR-3D) or chemical descriptors) parameters. Hereby, we describe a Graph Theory approach for generating and mining molecular fragments to be used in QSAR or QSPR modeling based exclusively on fragment contributions. Merging of Molecular Graph Theory, Simplified Molecular Input Line Entry Specification (SMILES) notation, and the connection table data allows a precise way to differentiate and count the molecular fragments. Machine learning strategies generated models with outstanding root mean square error (RMSE) and R2 values. We also present the software Charming QSAR & QSPR, written in Python, for the property prediction of chemical compounds while using this approach.

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Chinen, Kazue, and Timothy Malloy. "Multi-Strategy Assessment of Different Uses of QSAR under REACH Analysis of Alternatives to Advance Information Transparency." International Journal of Environmental Research and Public Health 19, no. 7 (April 4, 2022): 4338. http://dx.doi.org/10.3390/ijerph19074338.

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Under the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH) analysis of alternatives (AoA) process, quantitative structure–activity relationship (QSAR) models play an important role in expanding information gathering and organizing frameworks. Increasingly recognized as an alternative to testing under registration. QSARs have become a relevant tool in bridging data gaps and supporting weight of evidence (WoE) when assessing alternative substances. Additionally, QSARs are growing in importance in integrated testing strategies (ITS). For example, the REACH ITS framework for specific endpoints directs registrants to consider non-testing results, including QSAR predictions, when deciding if further animal testing is needed. Despite the raised profile of QSARs in these frameworks, a gap exists in the evaluation of QSAR use and QSAR documentation under authorization. An assessment of the different uses (e.g., WoE and ITS) in which QSAR predictions play a role in evidence gathering and organizing remains unaddressed for AoA. This study approached the disparity in information for QSAR predictions by conducting a substantive review of 24 AoA through May 2017, which contained higher-tier endpoints under REACH. Understanding the manner in which applicants manage QSAR prediction information in AoA and assessing their potential within ITS will be valuable in promoting regulatory use of QSARs and building out future platforms in the face of rapidly evolving technology while advancing information transparency.

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Worth, Andrew P. "ECVAM's Activities on Computer Modelling and Integrated Testing." Alternatives to Laboratory Animals 30, no. 2_suppl (December 2002): 133–37. http://dx.doi.org/10.1177/026119290203002s22.

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This paper introduces the basic concepts of quantitative structure–activity relationship (QSAR), expert system and integrated testing strategy, and explains how the analogy between QSARs and prediction models leads naturally to criteria for the validation of QSARs. ECVAM's in-house research programme on QSAR modelling and integrated testing is summarised, along with plans for the validation of QSAR models and expert system rulebases at the European Union level.

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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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Sizochenko, Natalia, and Jerzy Leszczynski. "Review of Current and Emerging Approaches for Quantitative Nanostructure-Activity Relationship Modeling." Journal of Nanotoxicology and Nanomedicine 1, no. 1 (January 2016): 1–16. http://dx.doi.org/10.4018/jnn.2016010101.

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Quantitative structure-activity/property relationships (QSAR/QSPR) approaches that have been applied with success in a number of studies are currently used as indispensable tools in the computational analysis of nanomaterials. Evolution of nano-QSAR methodology to the ranks of novel field of knowledge has resulted in the development of new so-called “nano-descriptors” and extension of the statistical approaches domain. This brief review focuses on the critical analysis of advantages and disadvantages of existing methods of nanoparticles' representation and their analysis in framework of structure-activity relationships. It summarizes recent QSAR/QSPR studies on inorganic nanomaterials. Here the authors describe practices for the QSAR modeling of inorganic nanoparticles, existing datasets, and discuss applicable descriptors and future perspectives of this field. About 50 different (Q)SAR/SPR models for inorganic nanomaterials have been developed during the past 5 years. An analysis of these peer reviewed publications shows that the most popular property of nanoparticles modeled via QSAR is their toxicity towards different bacteria, cell lines, and microorganisms. It has been clearly shown how nano-QSAR can contribute to the elucidation of toxicity mechanisms and different physical properties of inorganic nanomaterials.

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Rasulev, Bakhtiyor, and Gerardo Casanola-Martin. "QSAR/QSPR in Polymers." International Journal of Quantitative Structure-Property Relationships 5, no. 1 (January 2020): 80–88. http://dx.doi.org/10.4018/ijqspr.2020010105.

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Predictive modeling of the properties of polymers and polymeric materials is getting more attention, while it is still very complicated due to complexity of these materials. In this review, we discuss main applications of quantitative structure-property/activity relationships (QSPR/QSAR) methods for polymers published recently. The most relevant publications are discussed covering this field highlighting the main advantages and drawbacks of the obtained predictive models. Examples dealing with refractive index, glass transition temperatures, intrinsic viscosity, thermal decomposition and flammability properties are shown, together with a fouling-release activity study. Finally, some considerations are discussed in order to give some clues that could lead to the improvement in the efficient computational design and/or optimization of new polymers with enhanced properties/activities.

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Karelson, Mati, Uko Maran, Yilin Wang, and Alan R. Katritzky. "QSPR and QSAR Models Derived Using Large Molecular Descriptor Spaces. A Review of CODESSA Applications." Collection of Czechoslovak Chemical Communications 64, no. 10 (1999): 1551–71. http://dx.doi.org/10.1135/cccc19991551.

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An overview on the development of QSPR/QSAR equations using various descriptor-mining techniques and multilinear regression analysis in the framework of the CODESSA (Comprehensive Descriptors for Structural and Statistical Analysis) program is given. The description of the methodologies applied in CODESSA is followed by the presentation of the QSAR and QSPR models derived for eighteen molecular activities and properties. The properties cover single molecular species, interactions between different molecular species, properties of surfactants, complex properties and properties of polymers. A review with 54 references.

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Li, Yi-Xia, Abdul Rauf, Muhammad Naeem, Muhammad Ahsan Binyamin, and Adnan Aslam. "Valency-Based Topological Properties of Linear Hexagonal Chain and Hammer-Like Benzenoid." Complexity 2021 (April 22, 2021): 1–16. http://dx.doi.org/10.1155/2021/9939469.

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Topological indices are quantitative measurements that describe a molecule’s topology and are quantified from the molecule’s graphical representation. The significance of topological indices is linked to their use in QSPR/QSAR modelling as descriptors. Mathematical associations between a particular molecular or biological activity and one or several biochemical and/or molecular structural features are QSPRs (quantitative structure-property relationships) and QSARs (quantitative structure-activity relationships). In this paper, we give explicit expressions of two recently defined novel ev-degree- and ve-degree-based topological indices of two classes of benzenoid, namely, linear hexagonal chain and hammer-like benzenoid.

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Jorgensen, William L. "QSAR/QSPR and Proprietary Data." Journal of Chemical Information and Modeling 46, no. 3 (May 2006): 937. http://dx.doi.org/10.1021/ci0680079.

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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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Toropova, Alla P., and Andrey A. Toropov. "Evolution of Optimal Descriptors." International Journal of Quantitative Structure-Property Relationships 1, no. 2 (July 2016): 52–71. http://dx.doi.org/10.4018/ijqspr.2016070103.

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The quantitative structure - property / activity relationships (qsprs/qsars) analysis of different substances is an important area in mathematical and medicinal chemistry. The evolution and logic of optimal descriptors which are based on the monte carlo technique in the role of a tool of the qspr/qsar analysis is discussed. A group of examples of application of the optimal descriptors which are calculated with the coral software (http://www.insilico.eu/coral) for prediction of physicochemical and biochemical endpoints of potential therapeutical agents are presented. The perspectives and limitations of the optimal descriptors are listed. The attempt of the systematization of the models calculated with the coral software is the aim of this work.

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Turacı, Tufan, and Rafet Durgut. "On eccentricity-based topological indices of line and para-line graphs of some convex polytopes." Journal of Information and Optimization Sciences 44, no. 7 (2023): 1303–26. http://dx.doi.org/10.47974/jios-1217.

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Graph theory has been studied different areas such as mathematics, information and chemistry sciences. It is about descriptors in quantitative structure property relationship (QSPR) and quantitative structure activity relationship (QSAR) studies in the chemical network. Let G = (V(G), E(G)) be a graph without directed and multiple edges and without loops. A lot of topological indices have been defined for QSPR/QSAR studies. There are several types of these indices such as degree-based indices, eccentricity-based indices, and so on. The eccentricity-based topological indices are very important QSPR/QSAR studies, also recently these indices have been studied in many papers. In this paper, some eccentricity-based topological indices namely the connective eccentricity index xce(G), the eccentric connectivity index xc(G), the modified eccentric connectivity index xc(G), the total eccentricity index x(G), the Zagreb eccentricity indices M*1(G), M**1(G), M*2(G), the average eccentricity index avec(G), the eccentricity based geometric-arithmetic index GA4(G) and new version of ABC index such as ABC5(G) have been computed for line and para-line graphs of some convex polytopes Dn, Qn and Rn which are geometric graphs.

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Toropova, Alla, and Andrey Toropov. "Mutagenicity: QSAR - quasi-QSAR - nano-QSAR." Mini-Reviews in Medicinal Chemistry 15, no. 8 (April 28, 2015): 608–21. http://dx.doi.org/10.2174/1389557515666150219121652.

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Basant, Nikita, Shikha Gupta, and Kunwar P. Singh. "In silico prediction of the developmental toxicity of diverse organic chemicals in rodents for regulatory purposes." Toxicology Research 5, no. 3 (2016): 773–87. http://dx.doi.org/10.1039/c5tx00493d.

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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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Xu, Peng, Mehran Azeem, Muhammad Mubashir Izhar, Syed Mazhar Shah, Muhammad Ahsan Binyamin, and Adnan Aslam. "On Topological Descriptors of Certain Metal-Organic Frameworks." Journal of Chemistry 2020 (November 12, 2020): 1–12. http://dx.doi.org/10.1155/2020/8819008.

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Topological indices are numerical numbers that represent the topology of a molecule and are calculated from the graphical depiction of the molecule. The importance of topological indices is due to their use as descriptors in QSPR/QSAR modeling. QSPRs (quantitative structure-property relationships) and QSARs (quantitative structure-activity relationships) are mathematical correlations between a specified molecular property or biological activity and one or more physicochemical and/or molecular structural properties. In this paper, we give explicit expressions of some degree-based topological indices of two classes of metal-organic frameworks (MOFs), namely, butylated hydroxytoluene- (BHT-) based metal-organic ( M = Co , Fe, Mn, Cr) (MBHT) frameworks and M 1 TPyP − M 2 (TPyP = 5,10,15,20 -tetrakis(4-pyridyl)porphyrin and M 1 , M 2 = Fe and Co) MOFs.

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Hosamani, Sunilkumar M., Bhagyashri B. Kulkarni, Ratnamma G. Boli, and Vijay M. Gadag. "QSPR Analysis of Certain Graph Theocratical Matrices and Their Corresponding Energy." Applied Mathematics and Nonlinear Sciences 2, no. 1 (April 25, 2017): 131–50. http://dx.doi.org/10.21042/amns.2017.1.00011.

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AbstractIn QSAR/QSPR study, topological indices are utilized to guess the bioactivity of chemical compounds. In this paper, we study the QSPR analysis of certain graph theocratical matrices and their corresponding energy. Our study reveals some important results which helps to characterize the useful topological indices based on their predicting power.

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Mauri, Andrea, and Matteo Bertola. "Alvascience: A New Software Suite for the QSAR Workflow Applied to the Blood–Brain Barrier Permeability." International Journal of Molecular Sciences 23, no. 21 (October 25, 2022): 12882. http://dx.doi.org/10.3390/ijms232112882.

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Quantitative structure–activity relationship (QSAR) and quantitative structure–property relationship (QSPR) are established techniques to relate endpoints to molecular features. We present the Alvascience software suite that takes care of the whole QSAR/QSPR workflow necessary to use models to predict endpoints for untested molecules. The first step, data curation, is covered by alvaMolecule. Features such as molecular descriptors and fingerprints are generated by using alvaDesc. Models are built and validated with alvaModel. The models can then be deployed and used on new molecules by using alvaRunner. We use these software tools on a real case scenario to predict the blood–brain barrier (BBB) permeability. The resulting predictive models have accuracy equal or greater than 0.8. The models are bundled in an alvaRunner project available on the Alvascience website.

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Shirakol, Shailaja, Manjula Kalyanshetti, and Sunilkumar M. Hosamani. "QSPR Analysis of certain Distance Based Topological Indices." Applied Mathematics and Nonlinear Sciences 4, no. 2 (September 27, 2019): 371–86. http://dx.doi.org/10.2478/amns.2019.2.00032.

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AbstractIn QSAR/QSPR study, topological indices are utilized to guess the bioactivity of chemical compounds. In this paper, we study the QSPR analysis of selected distance and degree-distance based topological indices. Our study reveals some important results which help us to characterize the useful topological indices based on their predicting power.

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Chen, Xuan, Chang Ming Nie, and Song Nian Wen. "QSPR/QSAR Study of Mercaptans by Quantum Topological Method." Advanced Materials Research 233-235 (May 2011): 2536–40. http://dx.doi.org/10.4028/www.scientific.net/amr.233-235.2536.

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A new molecular quantum topological index QT was constructed by molecular topological methods and quantum mechanics (QM), which together with Gibbs free energy(G), Constant volume mole hot melting(CV) that were calculated by density functional theory (DFT) at the B3LYP/6-31G(d) level of theory for mercaptans. Index QT can not only efficiently distinguish molecular structures of mercaptans, but also possess good applications of QSPR/QSAR (quantitative structure-property/activity relationships). And most of the correlation coefficients of the models were over 0.99. The LOO CV (leave-one-out cross-validation) method was used to testify the stability and predictive ability of the models. The validation results verified the good stability and predictive ability of the models employing the cross-validation parameters: RCV, SCVand FCV, which demonstrated the wide potential of the index QT for applications to QSPR/ QSAR.

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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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Matsuzaka, Yasunari, and Yoshihiro Uesawa. "Computational Models That Use a Quantitative Structure–Activity Relationship Approach Based on Deep Learning." Processes 11, no. 4 (April 21, 2023): 1296. http://dx.doi.org/10.3390/pr11041296.

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In the toxicological testing of new small-molecule compounds, it is desirable to establish in silico test methods to predict toxicity instead of relying on animal testing. Since quantitative structure–activity relationships (QSARs) can predict the biological activity from structural information for small-molecule compounds, QSAR applications for in silico toxicity prediction have been studied for a long time. However, in recent years, the remarkable predictive performance of deep learning has attracted attention for practical applications. In this review, we summarize the application of deep learning to QSAR for constructing prediction models, including a discussion of parameter optimization for deep learning.

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Buchwald, Fabian, Tobias Girschick, Eibe Frank, and Stefan Kramer. "Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 1268–73. http://dx.doi.org/10.1609/aaai.v24i1.7494.

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Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to quantify the uncertainy of a QSAR prediction is to predict the conditional density of the activity given the structure instead of a point estimate. If a conditional density estimate is available, it is easy to derive prediction intervals of activities. In this paper, we experimentally evaluate and compare three methods for conditional density estimation for their suitability in QSAR modeling. In contrast to traditional methods for conditional density estimation, they are based on generic machine learning schemes, more specifically, class probability estimators. Our experiments show that a kernel estimator based on class probability estimates from a random forest classifier is highly competitive with Gaussian process regression, while taking only a fraction of the time for training. Therefore, generic machine-learning based methods for conditional density estimation may be a good and fast option for quantifying uncertainty in QSAR modeling.

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Gozalbes, Rafael, and Jesús Vicente de Julián-Ortiz. "Applications of Chemoinformatics in Predictive Toxicology for Regulatory Purposes, Especially in the Context of the EU REACH Legislation." International Journal of Quantitative Structure-Property Relationships 3, no. 1 (January 2018): 1–24. http://dx.doi.org/10.4018/ijqspr.2018010101.

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Chemoinformatics methodologies such as QSAR/QSPR have been used for decades in drug discovery projects, especially for the finding of new compounds with therapeutic properties and the optimization of ADME properties on chemical series. The application of computational techniques in predictive toxicology is much more recent, and they are experiencing an increasingly interest because of the new legal requirements imposed by national and international regulations. In the pharmaceutical field, the US Food and Drug Administration (FDA) support the use of predictive models for regulatory decision-making when assessing the genotoxic and carcinogenic potential of drug impurities. In Europe, the REACH legislation promotes the use of QSAR in order to reduce the huge amount of animal testing needed to demonstrate the safety of new chemical entities subjected to registration, provided they meet specific conditions to ensure their quality and predictive power. In this review, the authors summarize the state of art of in silico methods for regulatory purposes, with especial emphasis on QSAR models.

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Соснин, С. Б., Е. В. Радченко, В. А. Палюлин, and Н. С. Зефиров. "Обобщенный фрагментный подход в исследованиях QSAR/QSPR." Доклады Академии наук 463, no. 3 (2015): 297–300. http://dx.doi.org/10.7868/s0869565215210112.

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Karelson, Mati, Victor S. Lobanov, and Alan R. Katritzky. "Quantum-Chemical Descriptors in QSAR/QSPR Studies." Chemical Reviews 96, no. 3 (January 1996): 1027–44. http://dx.doi.org/10.1021/cr950202r.

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Estrada, Ernesto, and Enrique Molina. "3D Connectivity Indices in QSPR/QSAR Studies." Journal of Chemical Information and Computer Sciences 41, no. 3 (May 2001): 791–97. http://dx.doi.org/10.1021/ci000156i.

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Senese, Craig L., J. Duca, D. Pan, A. J. Hopfinger, and Y. J. Tseng. "4D-Fingerprints, Universal QSAR and QSPR Descriptors." Journal of Chemical Information and Computer Sciences 44, no. 5 (September 2004): 1526–39. http://dx.doi.org/10.1021/ci049898s.

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Lu, Chunhui, Weimin Guo, Xiaofang Hu, Yang Wang, and Chunsheng Yin. "A Lu index for QSAR/QSPR studies." Chemical Physics Letters 417, no. 1-3 (January 2006): 11–15. http://dx.doi.org/10.1016/j.cplett.2005.09.051.

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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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Sosnin, S. B., E. V. Radchenko, V. A. Palyulin, and N. S. Zefirov. "Generalized fragmental approach in QSAR/QSPR studies." Doklady Chemistry 463, no. 1 (July 2015): 185–88. http://dx.doi.org/10.1134/s0012500815070071.

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Miyao, Tomoyuki, Masamoto Arakawa, and Kimito Funatsu. "Exhaustive Structure Generation for Inverse-QSPR/QSAR." Molecular Informatics 29, no. 1-2 (January 12, 2010): 111–25. http://dx.doi.org/10.1002/minf.200900038.

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Hemmateenejad, Bahram, and Mahmood Sanchooli. "Substituent electronic descriptors for fast QSAR/QSPR." Journal of Chemometrics 21, no. 3-4 (2007): 96–107. http://dx.doi.org/10.1002/cem.1039.

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Nesměrák, Karel. "Medicinal Chemistry Meets Electrochemistry: Redox Potential in the Role of Endpoint or Molecular Descriptor in QSAR/QSPR." Mini-Reviews in Medicinal Chemistry 20, no. 14 (September 1, 2020): 1341–56. http://dx.doi.org/10.2174/1389557520666200204121806.

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Many biochemical reactions are based on redox reactions. Therefore, the redox potential of a chemical compound may be related to its therapeutic or physiological effects. The study of redox properties of compounds is a domain of electrochemistry. The subject of this review is the relationship between electrochemistry and medicinal chemistry, with a focus on quantifying these relationships. A summary of the relevant achievements in the correlation between redox potential and structure, therapeutic activity, resp., is presented. The first part of the review examines the applicability of QSPR for the prediction of redox properties of medically important compounds. The second part brings the exhaustive review of publications using redox potential as a molecular descriptor in QSAR of biological activity. Despite the complexity of medicinal chemistry and biological reactions, it is possible to employ redox potential in QSAR/QSPR. In many cases, this electrochemical parameter plays an essential but rarely absolute role.

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Khatri, Naveen, Harish Jangra, and A. K. Madan. "Path Pendeccentric Connectivity Indices: Detour Matrix Based Molecular Descriptors for QSAR/QSPR Studies, Part 1." International Journal of Quantitative Structure-Property Relationships 2, no. 2 (July 2017): 62–74. http://dx.doi.org/10.4018/ijqspr.2017070106.

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In the present study, highly sensitive detour matrix based molecular descriptors (MDs) termed as path pendeccentric connectivity indices 1-4 as well as their topochemical variants have been conceptualized. Proposed MDs are unique because they simultaneously take into consideration the cyclicity, path pendenticity, path eccentricity and augmented adjacency of each vertex in a hydrogen depleted molecular structure. An in-house computer program was also developed to calculate values of proposed MDs. Proposed MDs were evaluated for degeneracy, discriminating power, sensitivity towards relative position of substituent(s) in cyclic structures, branching and correlation with existing MDs. Highly encouraging results offer proposed MDs a vast potential for similarity/dissimilarity studies, characterization of structures, combinatorial library design, lead identification/optimization, pharmacokinetic relationship studies and QSAR/QSPR/QSTR studies. Proposed MDs will also take due care of shape factor, relative position (s) and presence of hetero atoms in chemical structures.

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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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M, Manjunath, Veeresh S. M, Pralahad M, and Rachanna Kanabur. "TOPOLOGICAL ASPECTS ON CORONENE GRAPH USING SOME GRAPH OPERATORS." South East Asian J. of Mathematics and Mathematical Sciences 19, no. 03 (December 30, 2023): 383–92. http://dx.doi.org/10.56827/seajmms.2023.1903.30.

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The Topological index is a numerical parameter of molecular graph which correlates its QSPR(Quantitative Structure Property Relationships) and QSAR(Quantitative Structure Activity Relationships). In this article, we compute topological indices of some graphs obtained from k-Coronene graph using some graph operations.

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Iqbal, Zahid, Muhammad Ishaq, Adnan Aslam, Muhammad Aamir, and Wei Gao. "The measure of irregularities of nanosheets." Open Physics 18, no. 1 (August 3, 2020): 419–31. http://dx.doi.org/10.1515/phys-2020-0164.

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AbstractNanosheets are two-dimensional polymeric materials, which are among the most active areas of investigation of chemistry and physics. Many diverse physicochemical properties of compounds are closely related to their underlying molecular topological descriptors. Thus, topological indices are fascinating beginning points to any statistical approach for attaining quantitative structure–activity (QSAR) and quantitative structure–property (QSPR) relationship studies. Irregularity measures are generally used for quantitative characterization of the topological structure of non-regular graphs. In various applications and problems in material engineering and chemistry, it is valuable to be well-informed of the irregularity of a molecular structure. Furthermore, the estimation of the irregularity of graphs is helpful for not only QSAR/QSPR studies but also different physical and chemical properties, including boiling and melting points, enthalpy of vaporization, entropy, toxicity, and resistance. In this article, we compute the irregularity measures of graphene nanosheet, H-naphtalenic nanosheet, {\text{SiO}}_{2} nanosheet, and the nanosheet covered by {C}_{3} and {C}_{6}.

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Selassie, C. D., R. Garg, and S. Mekapati. "Mechanism-based QSAR approach to the study of the toxicity of endocrine active substances." Pure and Applied Chemistry 75, no. 11-12 (January 1, 2003): 2363–73. http://dx.doi.org/10.1351/pac200375112363.

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Mechanism-based QSAR models for interactions between various ligands and the estrogenic receptor are developed by using well-developed physicochemical parameters. Common features of these QSARs are identified, and deficiencies in some datasets are highlighted. The relative binding affinities of various substituted hexestrols to estrogen receptors are examined in terms of their steric, electronic, and hydrophobic attributes. Different QSARs for hexestrols and tamoxifens reveal that steric effects are of overriding importance in variations of binding affinity. In the few cases where a large number of diverse substituents are located on aromatic rings, electronic effects emerge and suggest that electron-donating groups enhance binding to the receptor while hydrophobicity plays a marginal role in decreasing binding affinity. With substituted phenols bearing alkyl-type substituents and substituted hydroxy-biphenyls, the binding is strictly dependent on hydrophobicity and size. These QSAR models are described in detail and examined together to illustrate the utility of lateral validation in mechanistic interpretation.

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Ajmani, Subhash, Kamalakar Jadhav, and Sudhir A Kulkarni. "Group-Based QSAR (G-QSAR): Mitigating Interpretation Challenges in QSAR." QSAR & Combinatorial Science 28, no. 1 (November 18, 2008): 36–51. http://dx.doi.org/10.1002/qsar.200810063.

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Alov, Petko, Ivanka Tsakovska, and Ilza Pajeva. "Hybrid Classification/Regression Approach to QSAR Modeling of Stoichiometric Antiradical Capacity Assays’ Endpoints." Molecules 27, no. 7 (March 24, 2022): 2084. http://dx.doi.org/10.3390/molecules27072084.

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Quantitative structure–activity relationships (QSAR) are a widely used methodology allowing not only a better understanding of the mechanisms of chemical reactions, including radical scavenging, but also to predict the relevant properties of chemical compounds without their synthesis, isolation and experimental testing. Unlike the QSAR modeling of the kinetic antioxidant assays, modeling of the assays with stoichiometric endpoints depends strongly on the number of hydroxyl groups in the antioxidant molecule, as well as on some integral molecular descriptors characterizing the proportion of OH-groups able to enter and complete the radical scavenging reaction. In this work, we tested the feasibility of a “hybrid” classification/regression approach, consisting of explicit classification of individual OH-groups as involved in radical scavenging reactions, and using further the number of these OH-groups as a descriptor in simple-regression QSAR models of antiradical capacity assays with stoichiometric endpoints. A simple threshold classification based on the sum of trolox-equivalent antiradical capacity values was used, selecting OH-groups with specific radical stability- and reactivity-related electronic parameters or their combination as “active” or “inactive”. We showed that this classification/regression modeling approach provides a substantial improvement of the simple-regression QSAR models over those built on the number of total phenolic OH-groups only, and yields a statistical performance similar to that of the best reported multiple-regression QSARs for antiradical capacity assays with stoichiometric endpoints.

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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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Waisser, Karel, Milan Peřina, Věra Klimešová, and Jarmila Kaustová. "On the Relationship between the Structure and Antimycobacterial Activity of Substituted N-Benzylsalicylamides." Collection of Czechoslovak Chemical Communications 68, no. 7 (2003): 1275–94. http://dx.doi.org/10.1135/cccc20031275.

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Sixty-six N-benzylsalicylamides substituted in the acyl moiety in positions 3, 4 or 5 and in position 4 on the benzylic aromatic ring were synthesized. The compounds were tested for in vitro antimycobacterial activity against Mycobacterium tuberculosis, Mycobacterium kansasii and Mycobacterium avium. To evaluate structure-antimycobacterial activity relationships (QSARs), approaches based on the Free-Wilson as well as a combination of the Free-Wilson and Hansch methods were employed (substituent constants were used to describe the influence of the benzyl substituents, indicator parameters were used for the substituents on the acyl moiety). The use of the Hammett constants for benzyl substituents was not important for QSAR equations. The quadratic representation of lipophilicity parameters (π2) was significant only in QSAR equations of antimycobacterial activity against M. avium.

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Wang, Hui, Mingyue Jiang, Shujun Li, Chung-Yun Hse, Chunde Jin, Fangli Sun, and Zhuo Li. "Design of cinnamaldehyde amino acid Schiff base compounds based on the quantitative structure–activity relationship." Royal Society Open Science 4, no. 9 (September 2017): 170516. http://dx.doi.org/10.1098/rsos.170516.

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Cinnamaldehyde amino acid Schiff base (CAAS) is a new class of safe, bioactive compounds which could be developed as potential antifungal agents for fungal infections. To design new cinnamaldehyde amino acid Schiff base compounds with high bioactivity, the quantitative structure–activity relationships (QSARs) for CAAS compounds against Aspergillus niger ( A. niger ) and Penicillium citrinum (P. citrinum) were analysed. The QSAR models ( R 2 = 0.9346 for A. niger , R 2 = 0.9590 for P. citrinum, ) were constructed and validated. The models indicated that the molecular polarity and the Max atomic orbital electronic population had a significant effect on antifungal activity. Based on the best QSAR models, two new compounds were designed and synthesized. Antifungal activity tests proved that both of them have great bioactivity against the selected fungi.

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ABU ELELLA, MAHMOUD H., MARWA M. ABDEL-AZIZ, and NAHED A. ABD EL-GHANY. "SYNTHESIS OF A HIGH-PERFORMANCE ANTIMICROBIAL O-QUATERNIZED ALGINATE – A PROMISING POTENTIAL ANTIMICROBIAL AGENT." Cellulose Chemistry and Technology 55, no. 1-2 (February 12, 2021): 75–86. http://dx.doi.org/10.35812/cellulosechemtechnol.2021.55.08.

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Three novel biologically active quaternized sodium alginates were synthesized via the reaction of sodium alginate (SA) with 3-chloro-2-hydroxypropyl trimethylammonium chloride, at room temperature for different time intervals (1, 3 and 6 h), to produce quaternized sodium alginates designated as QSA1, QSA3 and QSA6. The percentage degree of quaternization (DQ%) significantly increased with increasing the reaction time. Images from FTIR, 1H-NMR, XRD and SEM have confirmed the chemical structures of the QSA samples. Their antimicrobial activity was investigated against bacteria and fungi using XTT assay, and the results showed that all QSA samples displayed high growth inhibition capacity of the tested microorganisms, compared to zero inhibition for SA, as shown by their lower minimum inhibitory concentration (MIC). The QSA6 was the best antimicrobial composite, displaying the same MIC value as that of the used reference drugs. The developed composites were found to be safe on normal human fibroblast cells (WI-38 cell line), by evaluating them using cytotoxic activity measurement, which makes QSA a promising material in biomedical and food applications.

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Singh, Dr Anamika, and Dr Rajeev Singh. "QSAR and its Role in Target-Ligand Interaction." Open Bioinformatics Journal 7, no. 1 (December 27, 2013): 63–67. http://dx.doi.org/10.2174/1875036201307010063.

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Each molecule has its own specialty, structure and function and when these molecules are combined together they form a compound. Structure and function of a molecule are related to each other and QSARs (Quantitative Structure– Activity relationships) are based on the criteria that the structure of a molecule must contain the features responsible for its physical, chemical, and biological properties, and on the ability to represent the chemical by one, or more, numerical descriptor(s). By QSAR models, the biological activity of a new or untested chemical can be inferred from the molecular structure of similar compounds whose activities have already been assessed. QSARs attempt to relate physical and chemical properties of molecules to their biological activities. For this there are so many descriptors (for example, molecular weight, number of rotatable bonds, Log P) and simple statistical methods such as Multiple Linear Regression (MLR) are used to predict a model. These models describe the activity of the data set and can predict activities for further sets of (untested) compounds. These types of descriptors are simple to calculate and allow for a relatively fast analysis. 3D-QSAR uses probe-based sampling within a molecular lattice to determine three-dimensional properties of molecules (particularly steric and electrostatic values) and can then correlate these 3D descriptors with biological activity. Physicochemical descriptors, include hydrophobicity, topology, electronic properties, and steric effects etc. These descriptors can be calculated empirically, statistically or through more recent computational methods. QSARs are currently being applied in many disciplines, with many pertaining to drug design and environmental risk assessment.

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Journal articles on the topic 'QSAR'

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