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Journal articles on the topic 'Cheminformatics and quantitative structure-activity relationships'

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Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Adl, Ammar, Moustafa Zein, and Aboul Ella Hassanien. "PQSAR: The membrane quantitative structure-activity relationships in cheminformatics." Expert Systems with Applications 54 (July 2016): 219–27. http://dx.doi.org/10.1016/j.eswa.2016.01.051.

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2

Javaid, Muhammad, and Muhammad Imran. "Editorial: Topological investigations of chemical networks." Main Group Metal Chemistry 44, no. 1 (January 1, 2021): 267–69. http://dx.doi.org/10.1515/mgmc-2021-0030.

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Abstract The topic of computing the topological indices (TIs) being a graph-theoretic modeling of the networks or discrete structures has become an important area of research nowadays because of its immense applications in various branches of the applied sciences. TIs have played a vital role in mathematical chemistry since the pioneering work of famous chemist Harry Wiener in 1947. However, in recent years, their capability and popularity has increased significantly because of the findings of the different physical and chemical investigations in the various chemical networks and the structures arising from the drug designs. In additions, TIs are also frequently used to study the quantitative structure property relationships (QSPRs) and quantitative structure activity relationships (QSARs) models which correlate the chemical structures with their physio-chemical properties and biological activities in a dataset of chemicals. These models are very important and useful for the research community working in the wider area of cheminformatics which is an interdisciplinary field combining mathematics, chemistry, and information science. The aim of this editorial is to arrange new methods, techniques, models, and algorithms to study the various theoretical and computational aspects of the different types of these topological indices for the various molecular structures.

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3

Dustigeer, Ghulam, Haidar Ali, Muhammad Imran Khan, and Yu-Ming Chu. "On multiplicative degree based topological indices for planar octahedron networks." Main Group Metal Chemistry 43, no. 1 (January 1, 2020): 219–28. http://dx.doi.org/10.1515/mgmc-2020-0026.

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Abstract Chemical graph theory is a branch of graph theory in which a chemical compound is presented with a simple graph called a molecular graph. There are atomic bonds in the chemistry of the chemical atomic graph and edges. The graph is connected when there is at least one connection between its vertices. The number that describes the topology of the graph is called the topological index. Cheminformatics is a new subject which is a combination of chemistry, mathematics and information science. It studies quantitative structure-activity (QSAR) and structure-property (QSPR) relationships that are used to predict the biological activities and properties of chemical compounds. We evaluated the second multiplicative Zagreb index, first and second universal Zagreb indices, first and second hyper Zagreb indices, sum and product connectivity indices for the planar octahedron network, triangular prism network, hex planar octahedron network, and give these indices closed analytical formulas.

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4

Rizwan, Muhammad, Akhlaq Ahmad Bhatti, Muhammad Javaid, and Fahd Jarad. "Some Bounds on Bond Incident Degree Indices with Some Parameters." Mathematical Problems in Engineering 2021 (July 8, 2021): 1–10. http://dx.doi.org/10.1155/2021/8417486.

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It is considered that there is a fascinating issue in theoretical chemistry to predict the physicochemical and structural properties of the chemical compounds in the molecular graphs. These properties of chemical compounds (boiling points, melting points, molar refraction, acentric factor, octanol-water partition coefficient, and motor octane number) are modeled by topological indices which are more applicable and well-used graph-theoretic tools for the studies of quantitative structure-property relationships (QSPRs) and quantitative structure-activity relationships (QSARs) in the subject of cheminformatics. The π -electron energy of a molecular graph was calculated by adding squares of degrees (valencies) of its vertices (nodes). This computational result, afterwards, was named the first Zagreb index, and in the field of molecular graph theory, it turned out to be a well-swotted topological index. In 2011, Vukicevic introduced the variable sum exdeg index which is famous for predicting the octanol-water partition coefficient of certain chemical compounds such as octane isomers, polyaromatic hydrocarbons (PAH), polychlorobiphenyls (PCB), and phenethylamines (Phenet). In this paper, we characterized the conjugated trees and conjugated unicyclic graphs for variable sum exdeg index in different intervals of real numbers. We also investigated the maximum value of SEIa for bicyclic graphs depending on a > 1 .

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Zheng, Jialin, Shehnaz Akhter, Zahid Iqbal, Muhammad Kashif Shafiq, Adnan Aslam, Muhammad Ishaq, and Muhammad Aamir. "Irregularity Measures of Subdivision Vertex-Edge Join of Graphs." Journal of Chemistry 2021 (January 18, 2021): 1–12. http://dx.doi.org/10.1155/2021/6673221.

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The study of graphs and networks accomplished by topological measures plays an applicable task to obtain their hidden topologies. This procedure has been greatly used in cheminformatics, bioinformatics, and biomedicine, where estimations based on graph invariants have been made available for effectively communicating with the different challenging tasks. Irregularity measures are mostly used for the characterization of the nonregular graphs. In several applications and problems in various areas of research like material engineering and chemistry, it is helpful to be well-informed about the irregularity of the underline structure. Furthermore, the irregularity indices of graphs are not only suitable for quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) studies but also for a number of chemical and physical properties, including toxicity, enthalpy of vaporization, resistance, boiling and melting points, and entropy. In this article, we compute the irregularity measures including the variance of vertex degrees, the total irregularity index, the σ irregularity index, and the Gini index of a new graph operation.

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6

Low, Yen S., Ola Caster, Tomas Bergvall, Denis Fourches, Xiaoling Zang, G. Niklas Norén, Ivan Rusyn, Ralph Edwards, and Alexander Tropsha. "Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome." Journal of the American Medical Informatics Association 23, no. 5 (October 24, 2015): 968–78. http://dx.doi.org/10.1093/jamia/ocv127.

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Abstract Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.

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7

Krawczyk, Bartosz. "Pattern recognition approach to classifying CYP 2C19 isoform." Open Medicine 7, no. 1 (February 1, 2012): 38–44. http://dx.doi.org/10.2478/s11536-011-0120-3.

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AbstractIn this paper a pattern recognition approach to classifying quantitative structure-property relationships (QSPR) of the CYP2C19 isoform is presented. QSPR is a correlative computer modelling of the properties of chemical molecules and is widely used in cheminformatics and the pharmaceutical industry. Predicting whether or not a particular chemical will be metabolized by 2C19 is of primary importance to the pharmaceutical industry. This task poses certain challenges. First of all analyzed data are characterized by a significant biological noise. Additionally the training set is unbalanced, with objects from negative class outnumbering the positives four times. Presented solution deals with those problems, additionally incorporating a throughout feature selection for improving the stability of received results. A strong emphasis is put on the outlier detection and proper model validation to achieve the best predictive power.

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Wang, Ying, Sumaira Hafeez, Shehnaz Akhter, Zahid Iqbal, and Adnan Aslam. "The Generalized Inverse Sum Indeg Index of Some Graph Operations." Symmetry 14, no. 11 (November 8, 2022): 2349. http://dx.doi.org/10.3390/sym14112349.

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The study of networks and graphs carried out by topological measures performs a vital role in securing their hidden topologies. This strategy has been extremely used in biomedicine, cheminformatics and bioinformatics, where computations dependent on graph invariants have been made available to communicate the various challenging tasks. In quantitative structure–activity (QSAR) and quantitative structure–property (QSPR) relationship studies, topological invariants are brought into practical action to associate the biological and physicochemical properties and pharmacological activities of materials and chemical compounds. In these studies, the degree-based topological invariants have found a significant position among the other descriptors due to the ease of their computing process and the speed with which these computations can be performed. Thereby, assessing these invariants is one of the flourishing lines of research. The generalized form of the degree-based inverse sum indeg index has recently been introduced. Many degree-based topological invariants can be derived from the generalized form of this index. In this paper, we provided the bounds related to this index for some graph operations, including the Kronecker product, join, corona product, Cartesian product, disjunction, and symmetric difference. We also presented the exact formula of this index for the disjoint union, linking, and splicing of graphs.

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9

Mumtaz, Hafiza Bushra, Muhammad Javaid, Hafiz Muhammad Awais, and Ebenezer Bonyah. "Topological Indices of Pent-Heptagonal Nanosheets via M-Polynomials." Journal of Mathematics 2021 (November 12, 2021): 1–13. http://dx.doi.org/10.1155/2021/4863993.

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The combination of mathematical sciences, physical chemistry, and information sciences leads to a modern field known as cheminformatics. It shows a mathematical relationship between a property and structural attributes of different types of chemicals called quantitative-structures’ activity and qualitative-structures’ property relationships that are utilized to forecast the chemical sciences and biological properties, in the field of engineering and technology. Graph theory has originated a significant usage in the field of physical chemistry and mathematics that is famous as chemical graph theory. The computing of topological indices (TIs) is a new topic of chemical graphs that associates many physiochemical characteristics of the fundamental organic compounds. In this paper, we used the M-polynomial-based TIs such as 1st Zagreb, 2nd Zagreb, modified 2nd Zagreb, symmetric division deg, general Randi c ´ , inverse sum, harmonic, and augmented indices to study the chemical structures of pent-heptagonal nanosheets of V C 5 C 7 and H C 5 C 7 . An estimation among the computed TIs with the help of numerical results is also presented.

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10

Bogdanović, Aleksandra, Anita Lazić, Slavica Grujić, Ivica Dimkić, Slaviša Stanković, and Slobodan Petrović. "Characterisation of twelve newly synthesised N-(substituted phenyl)-2-chloroacetamides with QSAR analysis and antimicrobial activity tests." Archives of Industrial Hygiene and Toxicology 72, no. 1 (March 1, 2021): 70–79. http://dx.doi.org/10.2478/aiht-2021-72-3483.

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Abstract In this study we screened twelve newly synthesised N-(substituted phenyl)-2-chloroacetamides for antimicrobial potential relying on quantitative structure-activity relationship (QSAR) analysis based on the available cheminformatics prediction models (Molinspiration, SwissADME, PreADMET, and PkcSM) and verified it through standard antimicrobial testing against Escherichia coli, Staphylococcus aureus, methicillin-resistant S. aureus (MRSA), and Candida albicans. Our compounds met all the screening criteria of Lipinski’s rule of five (Ro5) as well as Veber’s and Egan’s methods for predicting biological activity. In antimicrobial activity tests, all chloroacetamides were effective against Gram-positive S. aureus and MRSA, less effective against the Gram-negative E. coli, and moderately effective against the yeast C. albicans. Our study confirmed that the biological activity of chloroacetamides varied with the position of substituents bound to the phenyl ring, which explains why some molecules were more effective against Gram-negative than Gram-positive bacteria or C. albicans. Bearing the halogenated p-substituted phenyl ring, N-(4-chlorophenyl), N-(4-fluorophenyl), and N-(3-bromophenyl) chloroacetamides were among the most active thanks to high lipophilicity, which allows them to pass rapidly through the phospholipid bilayer of the cell membrane. They are the most promising compounds for further investigation, particularly against Gram-positive bacteria and pathogenic yeasts.

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Nolte, Tom M., and Willie J. G. M. Peijnenburg. "Aqueous-phase photooxygenation of enes, amines, sulfides and polycyclic aromatics by singlet (a1Δg) oxygen: prediction of rate constants using orbital energies, substituent factors and quantitative structure–property relationships." Environmental Chemistry 14, no. 7 (2017): 442. http://dx.doi.org/10.1071/en17155.

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Environmental contextTo aid the transition to sustainable chemistry there is a need to improve the degradability of chemicals and limit the use of organic solvents. Singlet oxygen, 1O2, is involved in organic synthesis and photochemical degradation; however, information on its aqueous-phase reactivity is limited. We developed cheminformatics models for photooxidation rate constants that will enable accurate assessment of aquatic photochemistry without experimentation. AbstractTo aid the transition to sustainable and green chemistry there is a general need to improve the degradability of chemicals and limit the use of organic solvents. In this study we developed quantitative structure–property relationships (QSPRs) for aqueous-phase photochemical reactions by singlet (a1Δg) oxygen. The bimolecular singlet oxygen reaction rate constant can be reliably estimated (R2 = 0.73 for naphtalenes and anthracenes, R2 = 0.86 for enes and R2 = 0.88 for aromatic amines) using the energy of the highest occupied molecular orbital (EHOMO). Additional molecular descriptors were used to characterise electronic and steric factors influencing the rate constant for aromatic enes (R2 = 0.74), sulfides and thiols (R2 = 0.72) and aliphatic amines. Mechanistic principles (frontier molecular orbital, perturbation and transition state theories) were applied to interpret the QSPRs developed and to corroborate findings in the literature. Depending on resonance, the speciation state (through protonation and deprotonation) can heavily influence the oxidation rate constant, which was accurately predicted. The QSPRs can be applied in synthetic photochemistry and for estimating chemical fate from photolysis or advanced water treatment.

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12

Basketter, D. A. "Quantitative structure-activity relationships." Toxicology in Vitro 3, no. 4 (January 1989): 351–53. http://dx.doi.org/10.1016/0887-2333(89)90044-1.

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13

Kumar, Praveen, Chinnappa Apattira Uthaiah, Santhosha Sangapurada Mahantheshappa, Nayak Devappa Satyanarayan, SubbaRao Venkata Madhunapantula, Hulikal Shivashankara Santhosh Kumar, and Rajeshwara Achur. "Antiproliferative potential, quantitative structure-activity relationship, cheminformatic and molecular docking analysis of quinoline and benzofuran derivatives." European Journal of Chemistry 11, no. 3 (September 30, 2020): 223–34. http://dx.doi.org/10.5155/eurjchem.11.3.223-234.2004.

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Quinoline and benzofuran moieties are commonly used for the synthesis of therapeutically beneficial molecules and drugs since they possess a wide range of pharmacological activities including potent anticancer activity as compared to other heterocyclic compounds. Many of well-known antimalarial, antimicrobial, anti-helminthic, analgesic, anti-inflammatory, antiprotozoal, and antitumor compounds contain quinoline/benzofuran skeleton. The aim of this study was to analyze ten new quinoline and eighteen benzofuran derivatives for carcinoma cell line growth inhibition and to predict possible interactions with the target. The anticancer activity of these compounds against colon cancer (HCT-116) and triple-negative breast cancer (MDA-MB-468) cell lines was determined and performed molecular docking to predict the possible interactions. Among ten quinoline derivatives, Q1, Q4, Q6, Q9, and Q10 were found to be the most potent against HCT-116 and MDA-MB-468 with IC50 values ranging from 6.2-99.6 and 2.7-23.6 μM, respectively. Using the IC50 values, a model equation with quantitative structure activity relationship (QSAR) was generated with their descriptors such as HBA1, HBA2, kappa (1, 2 and 3), Balaban index, Wiener index, number of rotatable bonds, log S, log P and total polar surface area (TPSA). The effect of benzofuran derivatives was moderate in cytotoxicity tests and hence only quinolines were considered for further analysis. The molecular docking indicated the mammalian / mechanistic target of rapamycin (mTOR), Topoisomerase I and II as possible targets for these molecules. The predicted results obtained from QSAR and molecular docking analysis of quinoline derivatives showed high correlation in comparison to the results of the cytotoxic assay. Overall, this study indicated that quinolines are more potent as anticancer agents compared to benzofurans. Further, compound Q9 has emerged as a lead molecule which could be the base for further development of more potent anticancer agents.

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14

Dunn, W. J. "Quantitative structure—activity relationships (QSAR)." Chemometrics and Intelligent Laboratory Systems 6, no. 3 (September 1989): 181–90. http://dx.doi.org/10.1016/0169-7439(89)80083-8.

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15

LIVINGSTONE, D. J. "ChemInform Abstract: Quantitative Structure-Activity Relationships." ChemInform 23, no. 5 (August 22, 2010): no. http://dx.doi.org/10.1002/chin.199205321.

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16

Parsons, J. R., and H. A. J. Govers. "Quantitative structure-activity relationships for biodegradation." Ecotoxicology and Environmental Safety 19, no. 2 (April 1990): 212–27. http://dx.doi.org/10.1016/0147-6513(90)90069-h.

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Safe, S., T. Sawyer, G. Mason, S. Bandiera, B. Keys, M. Romkes, J. Piskorska-Pliszczynska, B. Zmudzka, and L. Safe. "Polychlorinated dibenzofurans; quantitative structure activity relationships." Chemosphere 14, no. 6-7 (January 1985): 675–83. http://dx.doi.org/10.1016/0045-6535(85)90175-4.

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18

Gupta, Satya. "Quantitative Structure-Activity Relationships of Renin Inhibitors." Mini-Reviews in Medicinal Chemistry 3, no. 4 (June 1, 2003): 315–21. http://dx.doi.org/10.2174/1389557033488051.

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Gupta, Satya P. "Quantitative Structure-Activity Relationships of Antiarrhythmic Drugs." Current Pharmaceutical Design 4, no. 6 (December 1998): 455–68. http://dx.doi.org/10.2174/138161280406221011112729.

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Abstract: A Comprehensive review of quantitative structure-activity relationship (QSAR) studies on antiarrhythmic agents is presented. From the discussion point of view, the antiarrhythmic agents have been put into two broad classes: specific and nonspecific. While the main members of the former class can be -adrenergic blocking agents ( -blockers), any chemical that can act directly on the myocardial cell membrane, producing a cardiodepressant effect via changes in basic electrophysiological properties of the membrane, such as automaticity, excitability, conductivity, and refractoriness. has been put in the latter class. QSARs exhibit that the biological actions of a variety of drugs belonging to any class depend primarily on the Jipophilic haracter of the molecule or substituents. Thus, the hydrophobic interaction is found to play a dominant role in the action of antiarrhythmic agents. In certain cases, the QSARs also exhibit the role of electronic parameters, suggesting that certain receptors may have electronic site also, permitting the drugs to involve in some electronic interactions, too.

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20

Chohan, Kamaldeep, Stuart Paine, and Nigel Waters. "Quantitative Structure Activity Relationships in Drug Metabolism." Current Topics in Medicinal Chemistry 6, no. 15 (August 1, 2006): 1569–78. http://dx.doi.org/10.2174/156802606778108960.

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21

NAKAGAWA, Yoshiaki. "Quantitative Structure-Activity Relationships of Molting Inhibitors." Journal of Pesticide Science 21, no. 3 (1996): 363–77. http://dx.doi.org/10.1584/jpestics.21.363.

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22

Zakharova, E. A., P. V. Kosterin, and A. A. Shcherbakov. "Quantitative structure-activity relationships for auxin molecules." Biochemical Society Transactions 28, no. 5 (October 1, 2000): A408. http://dx.doi.org/10.1042/bst028a408.

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23

LIU, Qian, Shuichi HIRONO, and Ikuo MORIGUCHI. "Quantitative structure-activity relationships for calmodulin inhibitors." CHEMICAL & PHARMACEUTICAL BULLETIN 38, no. 8 (1990): 2184–89. http://dx.doi.org/10.1248/cpb.38.2184.

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Song, Xueqing, Alejandra Zapata, and George Eng. "Organotins and quantitative-structure activity/property relationships." Journal of Organometallic Chemistry 691, no. 8 (April 2006): 1756–60. http://dx.doi.org/10.1016/j.jorganchem.2005.12.003.

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Tiño, Peter, Ian T. Nabney, Bruce S. Williams, Jens Lösel, and Yi Sun. "Nonlinear Prediction of Quantitative Structure−Activity Relationships." Journal of Chemical Information and Computer Sciences 44, no. 5 (September 2004): 1647–53. http://dx.doi.org/10.1021/ci034255i.

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26

Oganesyan, É. T., S. Kh Chomaeva, A. V. Ivchenko, and L. S. Sarkisov. "New parameters in quantitative structure-activity relationships." Pharmaceutical Chemistry Journal 28, no. 10 (October 1994): 759–62. http://dx.doi.org/10.1007/bf02219311.

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Koch, Rainer, and Mathias Nagel. "Quantitative structure activity relationships in soil ecotoxicology." Science of The Total Environment 77, no. 2-3 (December 1988): 269–76. http://dx.doi.org/10.1016/0048-9697(88)90062-9.

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Cartier, A., and J. L. Rivail. "Electronic descriptors in quantitative structure—activity relationships." Chemometrics and Intelligent Laboratory Systems 1, no. 4 (October 1987): 335–47. http://dx.doi.org/10.1016/0169-7439(87)80039-4.

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Zare-Shahabadi, Vahid. "Quantitative structure–activity relationships of dihydrofolatereductase inhibitors." Medicinal Chemistry Research 25, no. 12 (August 19, 2016): 2787–97. http://dx.doi.org/10.1007/s00044-016-1666-z.

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Purcell, William Paul. "Quantitative structure-activity relationships of psychotropic agents." International Journal of Quantum Chemistry 9, S2 (June 18, 2009): 191–96. http://dx.doi.org/10.1002/qua.560090718.

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Nishimura, Keiichiro, Keiko Hirayama, Takamitsu Kobayashi, Toshio Fujita, and George Holan. "Quantitative structure-activity relationships of insecticidal diphenyldichlorocyclopropanes." Pesticide Biochemistry and Physiology 25, no. 2 (April 1986): 153–62. http://dx.doi.org/10.1016/0048-3575(86)90042-8.

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Nakagawa, Yoshiaki, Keiichi Izumi, Nobuhiro Oikawa, Akira Kurozumi, Hajime Iwamura, and Toshio Fujita. "Quantitative structure-activity relationships of benzoylphenylurea larvicides." Pesticide Biochemistry and Physiology 40, no. 1 (May 1991): 12–26. http://dx.doi.org/10.1016/0048-3575(91)90045-n.

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Dearden, John C. "Quantitative structure-activity relationships (QSAR) and odour." Food Quality and Preference 5, no. 1-2 (January 1994): 81–86. http://dx.doi.org/10.1016/0950-3293(94)90011-6.

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Barratt, M. D. "Quantitative structure-activity relationships for skin permeability." Toxicology in Vitro 9, no. 1 (February 1995): 27–37. http://dx.doi.org/10.1016/0887-2333(94)00190-6.

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Sanderson, Hans, and Marianne Thomsen. "Ecotoxicological Quantitative Structure–Activity Relationships for Pharmaceuticals." Bulletin of Environmental Contamination and Toxicology 79, no. 3 (August 16, 2007): 331–35. http://dx.doi.org/10.1007/s00128-007-9249-9.

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Gorbunov, S. M., Sh M. Yakubov, Zh V. Molodykh, L. A. Kudryavtseva, and I. S. Ryzhkina. "Quantitative structure-activity relationships in substituted orthoaminomethylphenols." Pharmaceutical Chemistry Journal 22, no. 9 (September 1988): 705–8. http://dx.doi.org/10.1007/bf00763668.

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Hasan, Riaz, Keiichiro Nishimura, and Tamio Ueno. "Quantitative structure-activity relationships of insecticidal pyrazolines." Pesticide Science 42, no. 4 (December 1994): 291–98. http://dx.doi.org/10.1002/ps.2780420406.

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Hermens, Joop L. M. "Quantitative structure-activity relationships in aquatic toxicology." Pesticide Science 17, no. 3 (June 1986): 287–96. http://dx.doi.org/10.1002/ps.2780170312.

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Kim, Pilho, Sunhee Kang, Helena I. Boshoff, Jan Jiricek, Margaret Collins, Ramandeep Singh, Ujjini H. Manjunatha, et al. "Structure−Activity Relationships of Antitubercular Nitroimidazoles. 2. Determinants of Aerobic Activity and Quantitative Structure−Activity Relationships." Journal of Medicinal Chemistry 52, no. 5 (March 12, 2009): 1329–44. http://dx.doi.org/10.1021/jm801374t.

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40

Rogers, David, and A. J. Hopfinger. "Application of Genetic Function Approximation to Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships." Journal of Chemical Information and Modeling 34, no. 4 (July 1, 1994): 854–66. http://dx.doi.org/10.1021/ci00020a020.

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Luke, Brian T. "Evolutionary Programming Applied to the Development of Quantitative Structure-Activity Relationships and Quantitative Structure-Property Relationships." Journal of Chemical Information and Modeling 34, no. 6 (November 1, 1994): 1279–87. http://dx.doi.org/10.1021/ci00022a009.

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Schmidt, Thomas, Amal Nour, Sami Khalid, Marcel Kaiser, and Reto Brun. "Quantitative Structure ‒ Antiprotozoal Activity Relationships of Sesquiterpene Lactones." Molecules 14, no. 6 (June 8, 2009): 2062–76. http://dx.doi.org/10.3390/molecules14062062.

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Hansch, Corwin, and Litai Zhang. "Quantitative Structure-Activity Relationships of Cytochrome P-450." Drug Metabolism Reviews 25, no. 1-2 (January 1993): 1–48. http://dx.doi.org/10.3109/03602539308993972.

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HAYAKAWA, Koichi, Akira NAKAYAMA, Hiroaki NISHIKAWA, Akira NAKATA, Shinsuke SANO, and Chinami YOKOTA. "Quantitative Structure-Activity Relationships of Fungicidal N-Phenylformamidoximes." Journal of Pesticide Science 17, no. 1 (1992): 17–25. http://dx.doi.org/10.1584/jpestics.17.17.

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Pontiki, Eleni, and Dimitra Hadjipavlou-Litina. "Quantitative Structure Activity Relationships (QSARs) on Lipoxygenase Inhibitors." Current Medicinal Chemistry - Anti-Inflammatory & Anti-Allergy Agents 3, no. 2 (June 1, 2004): 139–56. http://dx.doi.org/10.2174/1568014043355375.

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Farkas, Orsolya, Judit Jakus, and Károly Héberger. "Quantitative Structure – Antioxidant Activity Relationships of Flavonoid Compounds." Molecules 9, no. 12 (December 31, 2004): 1079–88. http://dx.doi.org/10.3390/91201079.

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Dearden, J. C. "Partitioning and lipophilicity in quantitative structure-activity relationships." Environmental Health Perspectives 61 (September 1985): 203–28. http://dx.doi.org/10.1289/ehp.8561203.

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Basketter, DA, Grace Patlewicz, E. Gimenez-Arnau, and J.-P. Lepoittevin. "P38 Quantitative structure activity relationships for fragrance aldehydes." Contact Dermatitis 50, no. 3 (June 28, 2008): 192. http://dx.doi.org/10.1111/j.0105-1873.2004.0309ft.x.

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Livingstone, David J. "Quantitative structure–activity relationships: correlation and similarity methods." Anal. Proc. 31, no. 3 (1994): 107–8. http://dx.doi.org/10.1039/ai9943100107.

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YOSHIOKA, YOSHITADA, YOUKI OSE, and TAKAHIKO SATO. "Quantitative structure-activity relationships in Tetrahymena toxicity studies." Eisei kagaku 32, no. 6 (1986): 464–69. http://dx.doi.org/10.1248/jhs1956.32.464.

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