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Letteratura scientifica selezionata sul tema "Quantitative Structure-Activity Relationship [MESH]"

Autore: Grafiati

Pubblicato: 29 luglio 2024

Ultima modifica: 30 luglio 2024

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Articoli di riviste sul tema "Quantitative Structure-Activity Relationship [MESH]"

1

Lin, Yu-Liang, Peng-Fei Fang, Xin Wang, Jie Wu e Guo-Lin Yang. "Experimental and Numerical Study on Tensile Behavior of Double-Twisted Hexagonal Gabion Wire Mesh". Buildings 13, n. 7 (28 giugno 2023): 1657. http://dx.doi.org/10.3390/buildings13071657.

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Double-twisted hexagonal gabion wire mesh is a type of reinforced soil material that is used in gabion retaining walls to stabilize the soil slope in geotechnical engineering. In this study, a series of tensile tests were conducted to investigate the tensile behavior of hexagonal gabion wire mesh. Meanwhile, numerical models of gabion wire mesh were built to investigate the whole tensile loading-strain process. The influence of wire diameter, mesh width, and mesh length on the tensile strength of hexagonal gabion wire mesh were evaluated based on laboratory tests and numerical simulation. The quantitative relationship of tensile strength versus wire diameter, mesh width, and mesh length was typically fitted by a quadratic function, linear function, and monotonically decreasing exponential function. The numerical result presents a good consistency with those obtained from the experiment. The result of the loading-strain curve obtained by both experiment and simulation exhibits an “S” shape with a distinct serrated characteristic. The loading-strain curve can be divided into the following four stages: mesh distortion stage, wire stretching stage, overall yield stage, and wire fracture stage, which well reflects the tensile behavior of double-twisted hexagonal wire mesh. The tensile behavior of gabion wire mesh is influenced by the structure pattern of wire mesh and the mechanical characteristic of steel wire.

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2

Ilyushchanka, Aliaksandr, Iryna Charniak, Aliaksei Kusin, Mihail Dechko, Ruslan Kusin e Natalia Rutkovskaia. "Selection of factors and preparation of an experiment planning matrix for modeling a filter material with an orthotropic structure based on woven meshes". MATEC Web of Conferences 366 (2022): 05001. http://dx.doi.org/10.1051/matecconf/202236605001.

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The advantages of a filtering material with an orthotropic structure (FMTS) consisting of a package of woven meshes are described. Information is given on the first stage of FMTS modeling, which includes the choice of parameters and factors of the experiment and the compilation of a planning matrix to establish the relationship between technological characteristics and properties of FMTS. When constructing a stochastic mathematical model at this stage, two factors were chosen to describe the properties of the material – qualitative (mesh type), specified by the sigma constraint method, and quantitative (package thickness). The constructed matrix of experiment planning is given. The natural and coded values of FMTS samples were determined.

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3

Pereira Gomes, Dione, e Aníbal Danilo Farias. "Systematic review on the relationship between left heart failure and right ventricular dysfunction in the 2000s". SCT Proceedings in Interdisciplinary Insights and Innovations 1 (10 novembre 2023): 143. http://dx.doi.org/10.56294/piii2023143.

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Background: Heart failure is a clinical syndrome characterized by symptoms such as dyspnea and fluid retention in the context of structural abnormalities of the heart. For many years, the importance of the right heart was ignored or forgotten, however, it has been revealed that the right ventricle is a key part in the prognosis of left heart failure. The right ventricle modulates the structure and function of the left ventricle. Interest arises in carrying out a bibliographic review on right heart failure as a cause of left heart failure. Material and methods: The study design was a systematic review of the literature available in PubMED, this review was of a quantitative-qualitative nature, through exhaustive and specific searches using MESH terminologies and boulean operators. Results: A total of 7 scientific articles were analyzed, with a total of 1134 patients, with a mean of 226. The most prevalent associated comorbidities were: arterial hypertension, diabetes, obesity and coronary disease. Conclusion: It is concluded that as a new therapeutic objective of HFpEF, something in which most of the authors of the analyzed articles agree, the focus should be placed on the treatment of right ventricular dysfunction.Right ventricular dysfunction is common and affects the normal function of the left ventricle, also impacting the symptoms and the chance of survival

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4

Liu, Jin Tao, Wei Shen, Qun Bo Fan e H. N. Cai. "Modeling the Cracking Process of the YSZ Thermal Barrier Coating under the Thermal Shocking Loads". Key Engineering Materials 512-515 (giugno 2012): 463–68. http://dx.doi.org/10.4028/www.scientific.net/kem.512-515.463.

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For low thermal conductivity and high corrosion resistance, yttria stabilized zirconia (YSZ), as a top coat (TC), is widely used in thermal barrier coatings (TBCs), and the micro-structure of the TC has significant effects on it thermal shock resistance. Combining digital image processing technique with finite element mesh generation methods, finite element (EF) models based on actual microstructures of plasma sprayed YSZ thermal barrier coatings are built in this paper, so as to simulate the coating’s dynamic failure process when suffering thermal shocking loads. The cracking process is revealed by calculating both the stress and strain evolutions within the coating. Based on the proposed method, the effects of porosity and distribution are further studied. The simulation results agree well with the experimental observation, indicating that the cracks are mainly caused by pore connectivity, which promotes the growth of cracks. This work is expected to be helpful to establish the quantitative relationship between the TBCs porosity and the coating’s service performance.

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5

Huang, Shunjie, Xiangqian Wang, Yingming Li, Lei Wang, Gang Liu, Fukun Xiao e O. V. Bashkov. "Analysis on Evolution Law of Small Structure Stress Arch and Composite Bearing Arch in Island Gob-Side Entry Driving". Geofluids 2022 (23 giugno 2022): 1–12. http://dx.doi.org/10.1155/2022/4303681.

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At present, the theory of supporting the surrounding rock small structure of gob-side entry driving has been widely used, but there is no specific quantitative analytical formula for the bearing strength and bearing characteristics of the structure. Construct a small structural stress arch mechanical model based on the arch axis equation, and divide the width of coal pillars (fractured zone-plastic softening zone-plastic hardening zone) and small structural stress arch height. According to the relationship between the stress arch height and the size of the roadway, the anchor cable length is determined to be 7.3 m, and the “anchor mesh + ordinary long anchor cable + grouting anchor cable” coordinated support plan is proposed: anchor net support is used for the first support, and long anchor cable and grouting anchor cable are used for the second support. Combined with the supporting parameters, a mechanical model of the surrounding rock composite bearing stress arch is proposed, and the composite bearing stress arch structure is derived using elastoplastic mechanics to obtain the ultimate bearing strength relationship expression. The results show that the ultimate bearing capacity of the haulage gateway of 17236 island working-face in the north of Zhangji coal mine can reach 29.193 MPa after the composite bearing stress arch support. The feasibility of the supporting scheme is verified, and field monitoring showed that the deformation zone of the surrounding rock of the transportation haulage gateway is stable after being supported by the composite bearing stress arch structure, the maximum shrinkage of the top and bottom of the roadway is 287 mm, and the distance between the two sides is 640 mm.

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Golubenko, Daniil, Farah Ejaz Ahmed e Nidal Hilal. "Novel Crosslinked Anion Exchange Membranes Based on Thermally Cured Epoxy Resin: Synthesis, Structure and Mechanical and Ion Transport Properties". Membranes 14, n. 6 (11 giugno 2024): 138. http://dx.doi.org/10.3390/membranes14060138.

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Limitations in existing anion exchange membranes deter their use in the efficient treatment of industrial wastewater effluent. This work presents an approach to fabricating novel anion-conducting membranes using epoxy resin monomers like hydrophobic or hydrophilic diglycidyl ether and quaternized polyethyleneimine (PEI). Manipulating the diglycidyl ether nature, the quantitative composition of the copolymer and the conditions of quaternization allows control of the physicochemical properties of the membranes, including water uptake (20.0–330%), ion exchange capacity (1.5–3.7 mmol/g), ionic conductivity (0.2–17 mS/cm in the Cl form at 20 °C), potentiostatic transport numbers (75–97%), as well as mechanical properties. A relationship was established between copolymer structure and conductivity/selectivity trade-off. The higher the quaternized polyethyleneimine, diluent fraction, and hydrophilicity of diglycidyl ether, the higher the conductivity and the lower the permselectivity. Hydrophobic diglycidyl ether gives a much better conductivity/selectivity ratio since it provides a lower degree of hydration than hydrophilic diglycidyl ether. Different mesh and non-woven reinforcing materials were also examined. The developed membranes demonstrate good stability in both neutral and acidic environments, and their benchmark characteristics in laboratory electrodialysis cells and batch-mode dialysis experiments are similar to or superior to, commercial membranes such as Neosepta© AMX, FujiFilm© Type1, and Fumasep FAD-PET.

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7

NAKAGAWA, Yoshiaki. "Quantitative Structure-Activity Relationship". Japanese Journal of Pesticide Science 38, n. 1 (2013): 1. http://dx.doi.org/10.1584/jpestics.w12-39.

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8

FUJITA, Toshio. "Quantitative structure-activity relationship and drug design." Journal of the agricultural chemical society of Japan 64, n. 1 (1990): 1–11. http://dx.doi.org/10.1271/nogeikagaku1924.64.1.

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9

Düren, Reiner, e Horst A. Diehl. "Quantitative structure-activity relationship of coumarin derivatives". Journal of Chromatography A 445 (gennaio 1988): 49–58. http://dx.doi.org/10.1016/s0021-9673(01)84507-6.

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10

De Benedetti, P. G. "Electrostatics in quantitative structure-activity relationship analysis". Journal of Molecular Structure: THEOCHEM 256 (aprile 1992): 231–48. http://dx.doi.org/10.1016/0166-1280(92)87169-z.

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Tesi sul tema "Quantitative Structure-Activity Relationship [MESH]"

1

Li, Ju-Yun. "Quantitative structure-activity relationship studies in medicinal chemistry". Case Western Reserve University School of Graduate Studies / OhioLINK, 1995. http://rave.ohiolink.edu/etdc/view?acc_num=case1062596938.

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2

Reddy, Badinehal Asrith. "COMMERCIALIZATION OF A QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIP TOOL - SARCHITECT". Case Western Reserve University School of Graduate Studies / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=case1295637833.

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3

Ren, Xin. "Quantitative structure-activity relationship based virtual screening for novel androgen receptor antagonists". Thesis, University of British Columbia, 2012. http://hdl.handle.net/2429/43293.

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Androgen receptor (AR) plays a critical role in prostate cancer development and progression. All current therapeutic AR inhibitors modulate the receptor via direct binding to its Hormone Binding Site (HBS). Despite the identification of other small molecule binding areas on the AR surface including Activation Function 2 (AF2), binding function 3 (BF3), and N-terminal domain (NTD), HBS continues to be the major target site for AR antagonists (even though this site is prone to resistant mutations). Thus, there is a high need for the identification and development of novel antagonists targeting HBS of the AR. In this study, an effective QSAR modeling pipeline was set up and proved to be capable of identifying new AR antagonists from a large ZINC collection of purchasable chemicals. In particular, we have utilized DRAGON, INDUCTIVE and MOE descriptors to create various binary QSAR models of anti-AR activity. When we have applied the developed QSAR solutions to screen more than 2 million chemicals from the ZINC database, we were able to identify 39 potential candidate AR HBS binders. When they were tested in the DHT displacement assay, 9 chemicals demonstrated the corresponding IC₅₀ values in efficient low-micromole range. Of those, 9 compounds later exhibited ability to inhibit AR in the eGFP transcriptional assay with the IC₅₀ values established at 1.04-16.18 μM level. Notably, 6 discovered chemicals demonstrated concentration-dependent suppression of survival of LNCaP prostate cancer cell lines. The results of this study set a ground for the development of an entire novel chemical class of AR antagonists that are distinct for the currently marketed drugs such as Nitalutamide, Flutomide, Cassodex, and MDV3100 that all share significant structural similarity.

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Smith, Mark David. "A quantitative structure-activity relationship (QSAR) study of the Ames mutagenicity assay". Thesis, University of Portsmouth, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.343333.

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In-vitro mutagenicity assays have traditionally been used for first line identification of potential genotoxic hazard, purporting to chemical carcinogenesis and heritable genetic damage. The recent advances m combinatorial chemistry and high throughput screening technologies have led to a massive explosion in numbers of possible therapeutic candidates being produced at the early stages of drug discovery. This rapid increase in the number of chemicals to be classified results in a greater need for to acquire alternative methods for the prediction of toxicity. Quantitative StructureActivity Relationships (QSAR) can till this need for early hazard identifications by elucidating the physicochemical basis of biological activity. The assumption with predictive QSARs for toxicity is that "biological activity may be described as a function of chemical constitution". This thesis focuses on the Ames mutagenicity assay data for two compound sets; one of 90 compounds, with limited structural flexibility, comprising a range of chemical classes (non-congeneric series), the second, a set of 30 flavonoid compounds. Three physicochemical descriptor sets were generated: EV A, a theoretical molecular descriptor based on the normal co-ordinate modes of vibration; WHIM, derived from weighting functions applied to the 3D-structural molecular co-ordinates; and TSAR, a series of hydrophobic, electronic and steric parameters traditionally associated with the production of biological QSARs. Various "unsupervised" data pre-treatment methods were adopted, to reduce the level of degeneracy within the individual descriptor sets, prior to the calculation of stepwise linear discriminant classification functions. Good predictive models for Ames mutagenicity, as determined by leave-one-out (jackknife) cross-validation, were obtained with each of the three physicochemical descriptor sets. An increase in the predictive ability was observed following the combination of variables from the individual descriptor sets, inferring that some unique information associated with mutagenic activity is contained within each descriptor set. The predictive stability of the models produced was assessed via independent compound predictions, with a poor overall success rate determined. This failure in external prediction was investigated and fundamental differences in physicochemical data space occupancy revealed. Conclusions on training set composition and general model applicability are made with consideration to individual model physicochemical data space coverage.

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5

Lanevskij, Kiril. "Absorption and Tissue Distribution of Drug-Like Compounds: Quantitative Structure-Activity Relationship Analysis". Doctoral thesis, Lithuanian Academic Libraries Network (LABT), 2011. http://vddb.laba.lt/obj/LT-eLABa-0001:E.02~2011~D_20111003_114235-89858.

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The objective of this work was to develop mechanistic quantitative structure activity relationship models that would facilitate the assessment of drug properties related to their absorption and distribution in the body. The analysis involved several parameters reflecting the rate of passive diffusion across brain endothelium and intestinal epithelium, and thermodynamic constants related to drug distribution between plasma and tissues. Permeation through cellular transport barriers was modeled by nonlinear equations relating the passive diffusion rate to physicochemical properties of drugs: lipophilicity, ionization, hydrogen bonding potential and molecular size. It was demonstrated that brain endothelium and intestinal epithelium exhibit a quantitatively similar pattern of permeability-ionization dependence – ionized species permeate 2-3 orders of magnitude slower than neutral molecules. Analysis of tissue to plasma partitioning data revealed the necessity to split original experimental values into separate terms reflecting plasma and tissue binding strength. Drugs’ affinity to tissues could then be described by their lipophilicity, whereas detrimental effect of ionization was only observed for acidic drugs. Finally, it was shown that a linear combination of quantitative blood-brain barrier transport parameters allows classifying drugs according to their access to central nervous system with 94% overall accuracy.
Šiame darbe pristatomi mechanistiniai kiekybinio struktūros ir aktyvumo ryšio modeliai, skirti vaistinių junginių savybių, charakterizuojančių jų absorbciją ir pasiskirstymą organizme prognozavimui. Nagrinėjama keletas parametrų, apibūdinančių paprastos difuzijos per biologines membranas greitį, taip pat termodinaminės konstantos, aprašančios vaistų pasiskirstymą tarp kraujo plazmos ir audinių. Ląstelinių pernašos barjerų pralaidumas buvo modeliuojamas netiesinėmis lygtimis, siejančiomis paprastos difuzijos greitį su vaistų fizikocheminėmis savybėmis, tokiomis kaip lipofiliškumas, jonizacija, vandenilinių ryšių sudarymo potencialas ir molekulių dydis. Nustatyta, kad smegenų endotelyje ir žarnyno epitelyje stebima panašaus pobūdžio difuzijos greičio priklausomybė nuo jonizacijos – katijonai ir anijonai difunduoja atitinkamai 2 ir 3 eilėmis lėčiau už neutralias molekules. Pademonstruota, kad analizuojant vaistų pasiskirstymo tarp audinių ir kraujo duomenis, būtina paversti pradines eksperimentines vertes kitais dydžiais, atspindinčiais vaistų jungimosi prie plazmos ir audinių komponentų stiprumą. Vaistų giminingumas audiniams gali būti aprašytas jų lipofiliškumu, o neigiama jonizacijos įtaka stebima tik rūgštiniams junginiams. Taip pat parodyta, kad vaistų pernašos per hematoencefalinę užtvarą kiekybinių parametrų tiesinė kombinacija leidžia 94% tikslumu klasifikuoti vaistus pagal jų prieinamumą centrinei nervų sistemai.

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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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Diaz-Perez, Maria-Jose. "Quantitative structure-activity relationship (QSAR) study of the effect of steroids on DNA replication". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/NQ50291.pdf.

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8

Peron, Jean-Marie. "Quantitative structure activity relationship analysis of anti-oxidants with central nervous system therapeutic potential". Thesis, King's College London (University of London), 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.271327.

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9

Jaafar, Mohd Zuli. "Chemometrics and pattern recognition methods with applications to environmental and quantitative structure-activity relationship studies". Thesis, University of Bristol, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.541608.

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Marsden-Jones, Siân Catherine. "The application of quantitative structure activity relationship models to the method development of countercurrent chromatography". Thesis, Brunel University, 2016. http://bura.brunel.ac.uk/handle/2438/12598.

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A fundamental challenge for liquid-liquid separation techniques such as countercurrent chromatography (CCC)and centrifugal partition chromatography (CPC), is the swift, efficient selection of the two phase solvent system containing more than two solvents, for the purification of pharmaceuticals and other molecules. A purely computational model that could predict the optimal solvent systems for separation using just molecular structure would be ideal for this task. The experimental value being predicted is the partition coefficient (Kd), which is the concentration of the compound in one phase divided by the concentration in the other. Using this approach, Quantitative Structure Activity Relationship (QSAR) models have been developed to predict the partitioning of compounds in two phase systems from the molecular structure of the compound using molecular descriptors. A Kd value in the range of 0.5 to 2 will give optimal separation. Molecular descriptors are varied, examples include logP values, hydrogen bond donor values and the number of oxygen atoms. This work describes how the QSAR models were developed and tested. A dataset of experimental logKd values for 54 compounds in six different combinations of four solvents (heptane, ethyl acetate, methanol and water) was used to train the QSAR models. A set of 196 possible molecular descriptors was generated for the 54 compounds and a partial least squares regression was used to identify which of these was significant in the relationship between logKd and molecular structure. The resulting models were used to predict the logKd values of four test compounds that had not been used to build the QSAR models. When these predictions were compared to the experimental logKd values, the root mean squared error for four of the six models was less than 0.5 and less than 0.7 for the remaining two. These models were used to successfully separate a range of structurally diverse pharmaceutical compounds by predicting the best solvent systems to carry out the separation on the CCC/CPC using nothing but their molecular structure.

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Libri sul tema "Quantitative Structure-Activity Relationship [MESH]"

1

Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud e Babak Sokouti. Quantitative Structure–Activity Relationship. Boca Raton : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076.

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2

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

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3

name, No. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. Boca Raton, FL: CRC Press, 2002.

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Romualdo, Benigni, a cura di. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. Boca Raton, Fla: CRC Press, 2003.

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5

Smith, Mark David. A quantitative structure-activity relationship (QSAR) study of the Ames mutagenicity assay. [Portsmouth?]: [University of Portsmouth?], 2000.

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6

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

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

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

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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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1956-, Devillers J., e Balaban Alexandru T, a cura di. Topological indices and related descriptors in QSAR and QSPR. Amsterdam: Gordon and Breach, 1999.

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Capitoli di libri sul tema "Quantitative Structure-Activity Relationship [MESH]"

1

Bajorath, Jürgen. "Quantitative Structure Activity Relationship". In Encyclopedia of Cancer, 1–5. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-27841-9_4882-2.

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Bajorath, Jürgen. "Quantitative Structure Activity Relationship". In Encyclopedia of Cancer, 3855–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-46875-3_4882.

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Bajorath, Jürgen. "Quantitative Structure Activity Relationship". In Encyclopedia of Cancer, 3128–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16483-5_4882.

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud e Babak Sokouti. "QSAR at a Glance". In Quantitative Structure–Activity Relationship, 1–3. Boca Raton : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-1.

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud e Babak Sokouti. "Database and Dataset". In Quantitative Structure–Activity Relationship, 5–9. Boca Raton : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-2.

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud e Babak Sokouti. "Molecular Descriptors". In Quantitative Structure–Activity Relationship, 11–24. Boca Raton : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-3.

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud e Babak Sokouti. "Descriptor Selection". In Quantitative Structure–Activity Relationship, 25–33. Boca Raton : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-4.

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud e Babak Sokouti. "Model Building". In Quantitative Structure–Activity Relationship, 35–51. Boca Raton : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-5.

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud e Babak Sokouti. "Validation of QSAR Models". In Quantitative Structure–Activity Relationship, 53–61. Boca Raton : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-6.

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Dastmalchi, Siavoush, Maryam Hamzeh-Mivehroud e Babak Sokouti. "Practical Example". In Quantitative Structure–Activity Relationship, 63–83. Boca Raton : CRC Press, [2018]: CRC Press, 2018. http://dx.doi.org/10.1201/9781351113076-7.

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Atti di convegni sul tema "Quantitative Structure-Activity Relationship [MESH]"

1

Du, Zhenjiao, e Yonghui Li. "Quantitative Structure-activity Relationship Study on Antioxidant Dipeptides". In 2022 AOCS Annual Meeting & Expo. American Oil Chemists' Society (AOCS), 2022. http://dx.doi.org/10.21748/cpyc1755.

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Abstract (sommario):

Antioxidative peptides have attracted increasing interest of researchers and consumers. Compared to wet chemistry methods, quantitative structure-activity relationship (QSAR) analysis as a in silicon method can be more efficient and cost effective and has been successfully applied to activity prediction of angiotensin I-converting enzyme inhibitory activity and bitterness of peptides. However, there are only few QSAR studies on antioxidative activity, particularly for dipeptides which have demonstrated ideal absorption ability in intestinal compared to larger peptides. This study aimed to conduct a comprehensive QSAR analysis between simple structure dipeptides and their antioxidative activity based on available results. 16 common amino acid descriptors were adopted and combined with partial least squares regression (PLSR) for modelling of ABTS and ORAC antioxidant activities of 75 dipeptides from literatures. Two descriptors, namely G-scale and VSW, were selected for further optimization of PLSR models for ABTS activity and ORAC activity, respectively. The two models resulted in R2 of 0.846 and 0.865 and Q2 of 0.737 and 0.756, respectively. With 20 times permutation, the R2 intercepts were 0.142 and 0.198, demonstrating the robustness of the models. Further, based on the two established models, the ABTS and ORAC activities of totally 400 dipeptides were predicted for screening. Overall, only a few descriptors can achieve acceptable performance in antioxidative activity modeling of dipeptides. The N-terminal residues have greater contribution to both ABTS and ORAC activity. Specifically, hydrophobicity played an important role in ABTS activity. The W, Y, E and L amino acid in the N-terminal of a dipeptide are likely to result in higher ABTS activity and the W and Y in the N-terminal of a dipeptide are likely to lead to higher ORAC activity. This study provides important guidance for future protein hydrolysis for antioxidant peptides, design, and selection of physicochemical properties and regression methods for model development.

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Luo, Huajun, Junzhi Wang, Yuan Zhou e Kun Zou. "Quantitative Structure-Activity Relationship of Sesquiterpene Lactones with Anti-Ulcerogenic Activity". In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5163275.

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Zhao, Li-Jiao, Ru-Gang Zhong e Yan Zhen. "Quantitative Structure-Activity Relationship Analysis of the Anticancer Activity of Chloroethylnitrosourea Derivatives". In 2007 1st International Conference on Bioinformatics and Biomedical Engineering. IEEE, 2007. http://dx.doi.org/10.1109/icbbe.2007.316.

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Li, Xiao-Li, Min Li, Zheng Ke, Hua-Jun Luo e Wei-Qiao Deng. "Quantitative Structure-Activity Relationship Model of Lenalidomide Analogues as TNF-Inhibitors". In 2013 Fifth International Conference on Computational and Information Sciences (ICCIS). IEEE, 2013. http://dx.doi.org/10.1109/iccis.2013.36.

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Neophytou, Katerina, Christos A. Nicolaou, Constantinos S. Pattichis e Christos N. Schizas. "Deriving Quantitative Structure-Activity Relationship Models Using Genetic Programming for Drug Discovery". In 6th International Special Topic Conference on Information Technology Applications in Biomedicine, 2007. IEEE, 2007. http://dx.doi.org/10.1109/itab.2007.4407401.

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Triatmaja, K., SY Prabawati e PD Widiakongko. "QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP (QSAR) STUDY OF EUGENOL DERIVATIVES AS ANTIOXIDANT COMPOUNDS". In International conference on food, nutrition, health and lifestyle. The International Institute of Knowledge Management, 2022. http://dx.doi.org/10.17501/26827026.2022.1102.

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Zhao Li-Jiao, Zhong Ru-Gang, Zhen Yan e Dai Qian-Huan. "Research on the Quantitative Structure-Carcinogenic Activity Relationship of N-Nitroso Compounds". In 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference. IEEE, 2005. http://dx.doi.org/10.1109/iembs.2005.1615532.

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S, Sushma, Nithish Sundaram e N. Jayapandian. "Machine learning based Unique Perfume Flavour Creation Using Quantitative Structure-Activity Relationship (QSAR)". In 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 2021. http://dx.doi.org/10.1109/iccmc51019.2021.9418246.

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Ridzuan, M. S. M., M. Z. Jaafar e M. M. Zain. "Quantitative structure-activity relationship (QSAR) modelling of N-aryl derivatives as cholinesterase inhibitors". In 2012 IEEE Symposium on Humanities, Science and Engineering Research (SHUSER). IEEE, 2012. http://dx.doi.org/10.1109/shuser.2012.6269006.

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Lu, Rong, Hai-xia Ma, Wei-ping Liu e Wen-xin Li. "Studies on quantitative structure-activity-relationship inhibitory effects of benzoic acid derivatives on tyrosinase". In 2010 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icbbe.2010.5516550.

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