Relevant bibliographies by topics / Cheminformatics and quantitative structure-activity relationships

Academic literature on the topic 'Cheminformatics and quantitative structure-activity relationships'

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Published: 10 December 2022
Last updated: 28 January 2023

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

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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Cheminformatics and quantitative structure-activity relationships"

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Girschick, Tobias [Verfasser], Burkhard [Akademischer Betreuer] Rost, and Stefan [Akademischer Betreuer] Kramer. "Enhanced Small Molecule Similarity for Quantitative Structure-Activity Relationship Modeling and Cheminformatics Applications / Tobias Girschick. Gutachter: Burkhard Rost ; Stefan Kramer. Betreuer: Burkhard Rost." München : Universitätsbibliothek der TU München, 2014. http://d-nb.info/1052995357/34.

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Chen, Jonathan Jun Feng. "Data Mining/Machine Learning Techniques for Drug Discovery: Computational and Experimental Pipeline Development." University of Akron / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1524661027035591.

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Bruce, Craig L. "Classification and interpretation in quantitative structure-activity relationships." Thesis, University of Nottingham, 2010. http://eprints.nottingham.ac.uk/11666/.

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A good QSAR model comprises several components. Predictive accuracy is paramount, but it is not the only important aspect. In addition, one should apply robust and appropriate statistical tests to the models to assess their significance or the significance of any apparent improvements. The real impact of a QSAR, however, perhaps lies in its chemical insight and interpretation, an aspect which is often overlooked. This thesis covers three main topics: a comparison of contemporary classifiers, interpretability of random forests and usage of interpretable descriptors. The selection of data mining technique and descriptors entirely determine the available interpretation. Using interpretable approaches we have demonstrated their success on a variety of data sets. By using robust multiple comparison statistics with eight data sets we demonstrate that a random forest has comparable predictive accuracies to the de facto standard, support vector machine. A random forest is inherently more interpretable than support vector machine, due to the underlying tree construction. We can extract some chemical insight from the random forest. However, with additional tools further insight would be available. A decision tree is easier to interpret than a random forest. Therefore, to obtain useful interpretation from a random forest we have employed a selection of tools. This includes alternative representations of the trees using SMILES and SMARTS. Using existing methods we can compare and cluster the trees in this representation. Descriptor analysis and importance can be measured at the tree and forest level. Pathways in the trees can be compared and frequently occurring subgraphs identified. These tools have been built around the Weka machine learning workbench and are designed to allow further additions of new functionality. The interpretability of a model is dependent on the model and the descriptors. They must describe something meaningful. To this end we have used the TMACC descriptors in the Solubility Challenge and literature data sets. We report how our retrospective analysis confirms existing knowledge and how we identify novel C-domain inhibition of ACE. In order to test our hypotheses we extended and developed existing software forming two applications. The Nottingham Cheminformatics Workbench (NCW) will generate TMACC descriptors and allows the user to build and analyse models, including visualising the chemical interpretation. Forest Based Interpretation (FBI) provides various tools for interpretating a random forest model. Both applications are written in Java with full documentation and simple installations wizards are available for Windows, Linux and Mac.
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McNeany, T. John. "Non-parametric approaches to quantitative structure-activity relationships." Thesis, University of Nottingham, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.431188.

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Andersson, Patrik. "Physico-chemical characteristics and quantitative structure-activity relationships of PCBs." Doctoral thesis, Umeå University, Chemistry, 2000. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-17.

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The polychlorinated biphenyls (PCBs) comprise a group of 209 congeners varying in the number of chlorine atoms and substitution patterns. These compounds tend to be biomagnified in foodwebs and have been shown to induce an array of effects in exposed organisms. The structural characteristics of the PCBs influence their potency as well as mechanism of action. In order to assess the biological potency of these compounds a multi-step quantitative structure-activity relationship (QSAR) procedure was used in the project described in this thesis.

The ultraviolet absorption (UV) spectra were measured for all 209 PCBs, and digitised for use as physico-chemical descriptors. Interpretations of the spectra using principal component analysis (PCA) showed the number of ortho chlorine atoms and para-para substitution patterns to be significant. Additional physico-chemical descriptors were derived from semi-empirical calculations. These included various molecular energies, the ionisation potential, electron affinity, dipole moments, and the internal barrier of rotation. The internal barrier of rotation was especially useful for describing the conformation of the PCBs on a continuous scale.

In total 52 physico-chemical descriptors were compiled and analysed by PCA for the tetra- to hepta-chlorinated congeners. The structural variation within these compounds was condensed into four principal properties derived from a PCA for use as design variables in a statistical design to select congeners representative for these homologue-groups. The 20 selected PCBs have been applied to study structure-specific biochemical responses in a number of bioassays, and to study the biomagnification of the PCBs in various fish species.

QSARs were established using partial least squares projections to latent structures (PLS) for the PCBs potency to inhibit intercellular communication, activate respiratory burst, inhibit dopamine uptake in synaptic vesicles, compete with estradiol for binding to estrogen receptors, and induce cytochrome P4501A (CYP1A) related activities. By the systematic use of the designed set of PCBs the biological potency was screened over the chemical domain of the class of compounds. Further, sub-regions of highly potent PCBs were identified for each response measured. For risk assessment of the PCBs potency to induce dioxin-like activities the predicted induction potencies (PIPs) were calculated. In addition, two sets of PCBs were presented that specifically represent congeners of environmental relevance in combination with predicted potency to induce estrogenic and CYP1A related activities.

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Cronin, Mark T. D. "Quantitative structure-activity relationships of comparative toxicity to aquatic organisms." Thesis, Liverpool John Moores University, 1990. http://researchonline.ljmu.ac.uk/4989/.

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Quantitative Structure-Activity relationship (QSAR) attempt statistically to relate the physico-chemical properties of a molecule to its biological activity. A QSAR analysis was performed on the toxicities of up to 75 organic chemicals to two aquatic species, Photobacterium phospherum (known as the Microtox test), and the fathead minnow. To model the toxicities 49 physico-chemical and structural parameters were produced including measures of hydrophobicity, molecular size and electronic effects from techniques such as computational chemistry and the use of molecular connectivity indices. These were reduced to a statistically more manageable number by cluster analysis, principal component analysis, factor analysis, and canonical correlation analysis. The de-correlated data were then used to form relationships with the toxicities. All the techniques were validated using a testing set. Some good predictions of toxicity came from regression analysis of the original de-correlated variables. Although successful in simplifying the complex data matrix, principal component analysis, factor analysis, and canonical content analysis were disappointing as predictors of toxicity. The performance of each of the statistical techniques is discussed. The inter-species relationships of toxicity between four Commonly utilised aquatic endpoints, fathead minnow 96 hour IC50, Microtox 5 minute EC50, Daphnia magna 48 hour IC50, and Tetrahymena pyriformis 60 hour IG50, were investigated. Good relationships was found between the fathead minnow and both T. pyriformis and D. magna toxicities indicating that these species could be used to model fish toxicity. The outliers from individual relationships were assessed in order to elucidate if any molecular features may be causing greater relative toxicity in one species as compared to another. It is concluded that in addition to the intrinsic differences between species, the greater length of the test time for any species may result in increases bioaccumulation, metabolism, and detoxification of certain chemical classes. The relationships involving fish toxicity were moderately improved by the addition of a hydrophobic parameter.
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Gooch, Carolyn A. "Quantitative structure-activity relationships : a biophysical, chemical and calorimetric study." Thesis, Royal Holloway, University of London, 1988. http://repository.royalholloway.ac.uk/items/26719d55-b208-4995-bef0-92e4f0f80c0e/1/.

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Quantitative structure-activity relationships (QSAR) rationalize interrelation between molecular structure and biological response in terms of either physicochemical parameters, as in linear free energy relationships (LFER), or via purely empirical parameters, as is the case for De Novo schemes. In LFER the leading process is often the partitioning of a compound between two solvent phases, taken to represent the transfer of a drug molecule across a biological membrane. This study has investigated the partitioning behaviour of three series of hydroxybenzoate esters, viz. o-, m- and predominantly p-esters, the latter being preservatives in pharmaceutical formulations. The thermodynamic parameters AH, AG and AS for the transfer process were derived in an attempt to establish a QSAR. on a fundamental thermodynamic basis. Such parameters have identifiable physicochemical meaning and lend themselves more readily to interpretation. This facilitates application to alternative systems. A new Gibbs function factor analysis was developed and utilized to obtain thermodynamic contributions for parent and incremental methylene group portions of thestudy molecules. The empirical Collander equation for interrelation of various solute/solvent systems was also rationalized on a thermodynamic basis. Further extension of the Gibbs function factor analysis allowed scaling of "solvent" systems including chromatographic packings, solvents and liposomes. The scheme indicated capacity for optimized selection of bulk solvent systems to mimic biological membranes. A novel analytical procedure for direct measurement of biological response was developed. The bioassay appeared capable of discrimination i) between the closely related structural homologues, ii) between gram-negative and gram-positive bacteria, and further, iii) between certain cell batches of the same bacteria type. Also, the bioassay demonstrated a Collander interrelation between the two bacteria types. Flow microcalorimetry was the technique employed to measure thermal response of respiring E. coli and Staph, aur. bacteria. The modification of biological response with drug concentration was quantitated and a log dose max term was derived for each homologue. The results indicated potential for a predictive, additive structure-activity scheme based on assessment of biological response (BR) direct rather than through f(BR) via physicochemical or empirical parameters.
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Ruark, Christopher Daniel. "Quantitative Structure-Activity Relationships for Organophosphates Binding to Trypsin and Chymotrypsin." Wright State University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=wright1278010674.

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Stanforth, Robert William. "Extending K-Means clustering for analysis of quantitative structure activity relationships (QSAR)." Thesis, Birkbeck (University of London), 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.500005.

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A Quantitative Structure-Activity Relationship (QSAR) study is an attempt to model some biological activity over a collection of chemical compounds in terms of their structural properties A QSAR model may be constructed through (typically linear) multivariate regression analysis of the biological activity data against a number of features or 'descriptors' of chemical structure. As with any regression model, there are a number of issues emerging in real applications, including (a) domain of applicability of the model, (b) validation of the model within its domain of applicability, and (c) possible non-linearity of the QSAR Unfortunately the existing methods commonly used in QSAR for overcoming these issues all suffer from problems such as computational inefficiency and poor treatment of non- linearity. In practice this often results in the omission of proper analysis of them altogether. In this thesis we develop methods for tackling the issues listed above using K-means clustering. Specifically, we model the shape of a dataset in terms of intelligent K-means clustering results and use this to develop a non- parametric estimate for the domain of applicability of a QSAR model. Next we propose a 'hybrid' variant of K-means, incorporating a regression-wise element, which engenders a technique for non-linear QSAR modelling. Finally we demonstrate how to partition a dataset into training and testing subsets, using the K-means clustering to ensure that the partitioning respects the overall distribution Our experiments involving real QSAR data confirm the effectiveness of the methods developed in the project.
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Mwense, Mulaisho. "Toxicity prediction of mixtures of organic chemicals using quantitative structure-activity relationships." Thesis, University of Leeds, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.418230.

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Books on the topic "Cheminformatics and quantitative structure-activity relationships"

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Alexandre, Varnek, Tropsha Alex, and Royal Society of Chemistry (Great Britain)., eds. Chemoinformatics approaches to virtual screening. Cambridge: RSC Pub., 2008.

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International Workshop on Quantitative Structure-Activity Relationships in Environmental Sciences (7th 1996 Elsinore, Denmark). Quantitative structure-activity relationships in environmental sciences, VII. Pensacola, Fla: SETAC Press, 1997.

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Mager, Peter P. Multivariate chemometrics in QSAR (quantitative structure-activity relationships): A dialogue. Letchworth, Hertfordshire, England: Research Studies Press, 1988.

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Reduced rank regression: With applications to quantitative structure-activity relationships. Heidelberg: Physica-Verlag, 1995.

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Roy, Kunal. Quantitative structure-activity relationships in drug design, predictive toxicology, and risk assessment. Hershey PA: Medical Information Science Reference, an imprint of IGI Global, 2015.

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Martin, Yvonne Connolly. Quantitative drug design: A critical introduction. 2nd ed. Boca Raton: CRC Press/Taylor & Francis, 2010.

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Martin, Yvonne Connolly. Quantitative drug design: A critical introduction. 2nd ed. Boca Raton, FL: Taylor & Francis, 2010.

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1931-, Karcher W., and Devillers J. 1956-, eds. Practical applications of quantitative structure-activity relationships (QSAR) in environmental chemistry and toxicology. Dordrecht: Kluwer Academic Publishers, 1990.

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Martin, Yvonne Connolly. Quantitative drug design: A critical introduction. 2nd ed. Boca Raton, FL: Taylor & Francis, 2010.

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Quantitative drug design: A critical introduction. 2nd ed. Boca Raton, FL: Taylor & Francis, 2010.

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Book chapters on the topic "Cheminformatics and quantitative structure-activity relationships"

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Kuhn, Max. "Quantitative-Structure Activity Relationship Modeling and Cheminformatics." In Nonclinical Statistics for Pharmaceutical and Biotechnology Industries, 141–55. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23558-5_6.

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Garg, Rajni. "Cheminformatics and Comparative Quantitative Structure-Activity Relationship." In ACS Symposium Series, 97–110. Washington, DC: American Chemical Society, 2005. http://dx.doi.org/10.1021/bk-2005-0894.ch007.

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Klebe, Gerhard. "Quantitative Structure–Activity Relationships." In Drug Design, 371–96. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-17907-5_18.

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Schmidli, Heinz. "Quantitative Structure Activity Relationships (QSAR)." In Contributions to Statistics, 5–15. Heidelberg: Physica-Verlag HD, 1995. http://dx.doi.org/10.1007/978-3-642-50015-2_2.

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García-Sánchez, Mario Omar, Maykel Cruz-Monteagudo, and José L. Medina-Franco. "Quantitative Structure-Epigenetic Activity Relationships." In Challenges and Advances in Computational Chemistry and Physics, 303–38. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56850-8_8.

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Davis, Andrew M. "CHAPTER 6. Quantitative Structure–Activity Relationships." In The Handbook of Medicinal Chemistry, 154–83. Cambridge: Royal Society of Chemistry, 2015. http://dx.doi.org/10.1039/9781782621836-00154.

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Gupta, Satya P. "Quantitative structure-activity relationships of antianginal drugs." In Progress in Drug Research, 121–54. Basel: Birkhäuser Basel, 2001. http://dx.doi.org/10.1007/978-3-0348-8319-1_3.

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Gupta, Satya P. "Quantitative structure - activity relationships of cardiotonic agents." In Progress in Drug Research, 235–82. Basel: Birkhäuser Basel, 2000. http://dx.doi.org/10.1007/978-3-0348-8385-6_7.

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Kubinyi, Hugo. "Current Problems in Quantitative Structure Activity Relationships." In Physical Property Prediction in Organic Chemistry, 235–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-74140-1_17.

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Maguna, F. P., N. B. Okulik, and Eduardo A. Castro. "Quantitative Structure–Activity Relationships of Antimicrobial Compounds." In Handbook of Computational Chemistry, 1343–57. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-0711-5_38.

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Conference papers on the topic "Cheminformatics and quantitative structure-activity relationships"

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Liu, Rong, Robert Rallo, and Yoram Cohen. "Quantitative Structure-Activity-Relationships for cellular uptake of nanoparticles." In 2013 IEEE 13th International Conference on Nanotechnology (IEEE-NANO). IEEE, 2013. http://dx.doi.org/10.1109/nano.2013.6720861.

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Butkiewicz, Mariusz, Ralf Mueller, Danilo Selic, Eric Dawson, and Jens Meiler. "Application of machine learning approaches on quantitative structure activity relationships." In 2009 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2009. http://dx.doi.org/10.1109/cibcb.2009.4925736.

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Xu, Jingbo, and Tisong Jing. "Quantitative Structure-Activity Relationships for Oryzias Latipes Gill ATPase Endpoint." In 2008 2nd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '08). IEEE, 2008. http://dx.doi.org/10.1109/icbbe.2008.296.

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Al-Amood, Hanoy K., Hanan F. Al-Shamsi, and Hayat H. Abbas. "Quantitative structure-activity relationships of some new beta amino-carbonyl compounds." In INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2019. AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0029650.

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Kimani, Njogu, Josphat Matasyoh, Marcel Kaiser, Mauro Nogueira, Gustavo Trossini, and Thomas Schmidt. "An extended study on quantitative structure-antitrypanosomal activity relationships of sesquiterpene lactones." In 4th International Electronic Conference on Medicinal Chemistry. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/ecmc-4-05591.

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Edgar, Julie A., and Susie Hurley. "The Use of Quantitative Structure Activity Relationships (QSAR) in Traction Fluid Design." In 2004 SAE Fuels & Lubricants Meeting & Exhibition. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2004. http://dx.doi.org/10.4271/2004-01-2009.

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LABUTE, P. "BINARY QSAR: A NEW METHOD FOR THE DETERMINATION OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789814447300_0044.

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Gackowski, Marcin, Karolina Szewczyk-Golec, and Marcin Koba. "MARSplines Approach for Quantitative Relationships between Structure and Pharmacological Activity of Potential Drug Candidates." In ECMC 2022. Basel Switzerland: MDPI, 2022. http://dx.doi.org/10.3390/ecmc2022-13170.

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Hoekman, Leo, Peng Li, Litai Zhang, and Hansch. "A comprehensive approach to database management of quantitative structure-activity relationships (QSAR) in chemistry and biology." In Proceedings of the Twenty-Seventh Annual Hawaii International Conference on System Sciences. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/hicss.1994.323577.

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Stitou, Mourad, Hamid Toufik, Mohammed Bouachrine, Hssain Bih, and Fatima Lamchouri. "Machine learning algorithms used in Quantitative structure-activity relationships studies as new approaches in drug discovery." In 2019 International Conference on Intelligent Systems and Advanced Computing Sciences (ISACS). IEEE, 2019. http://dx.doi.org/10.1109/isacs48493.2019.9068917.

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Reports on the topic "Cheminformatics and quantitative structure-activity relationships"

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Turner, J. E. (Quantitative structure-activity relationships in environmental toxicology). Office of Scientific and Technical Information (OSTI), October 1990. http://dx.doi.org/10.2172/6613721.

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