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Artykuły w czasopismach na temat "QSAR Model"
Li, Yan Kun, i Xiao Ying Ma. "QSAR/QSPR Model Research of Complicated Samples". Advanced Materials Research 740 (sierpień 2013): 306–9. http://dx.doi.org/10.4028/www.scientific.net/amr.740.306.
Pełny tekst źródłaOkey, Robert W., i H. David Stensel. "A QSAR-based biodegradability model—A QSBR". Water Research 30, nr 9 (wrzesień 1996): 2206–14. http://dx.doi.org/10.1016/0043-1354(96)00098-x.
Pełny tekst źródłaZhang, Xiujun, H. G. Govardhana Reddy, Arcot Usha, M. C. Shanmukha, Mohammad Reza Farahani i Mehdi Alaeiyan. "A study on anti-malaria drugs using degree-based topological indices through QSPR analysis". Mathematical Biosciences and Engineering 20, nr 2 (2022): 3594–609. http://dx.doi.org/10.3934/mbe.2023167.
Pełny tekst źródłaToropov, Andrey A., i Alla P. Toropova. "The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR". Current Computer-Aided Drug Design 16, nr 3 (2.06.2020): 197–206. http://dx.doi.org/10.2174/1573409915666190328123112.
Pełny tekst źródłaMudasir, Mudasir, Iqmal Tahir i Ida Puji Astuti Maryono Putri. "QUANTITATIVE STRUCTURE AND ACTIVITY RELATIONSHIP ANALYSIS OF 1,2,4-THIADIAZOLINE FUNGICIDES BASED ON MOLECULAR STRUCTURE CALCULATED BY AM1 METHOD". Indonesian Journal of Chemistry 3, nr 1 (7.06.2010): 39–47. http://dx.doi.org/10.22146/ijc.21904.
Pełny tekst źródłaSarkar, Bikash Kumar. "DFT Based QSAR Studies of Phenyl Triazolinones of Protoporphyrinogen Oxidase Inhibitors". Asian Journal of Organic & Medicinal Chemistry 5, nr 4 (31.12.2020): 307–11. http://dx.doi.org/10.14233/ajomc.2020.ajomc-p280.
Pełny tekst źródłaRybińska-Fryca, Anna, Anita Sosnowska i Tomasz Puzyn. "Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids". Materials 13, nr 11 (30.05.2020): 2500. http://dx.doi.org/10.3390/ma13112500.
Pełny tekst źródłaPokle, Maithili S., Rashmi D. Singh i Madhura P. Vaidya. "2D QSAR MODEL BASED ON 1,2-DISUBSTITUTED BENZIMIDAZOLES IMPDH INHIBITORS". Indian Drugs 59, nr 04 (1.06.2022): 18–23. http://dx.doi.org/10.53879/id.59.04.13117.
Pełny tekst źródłaBu, Qingwei, Qingshan Li, Yun Liu i Chun Cai. "Performance Comparison between the Specific and Baseline Prediction Models of Ecotoxicity for Pharmaceuticals: Is a Specific QSAR Model Inevitable?" Journal of Chemistry 2021 (31.10.2021): 1–8. http://dx.doi.org/10.1155/2021/5563066.
Pełny tekst źródłaLIAO, SI YAN, LI QIAN, JIN CAN CHEN, YONG SHEN i KANG CHENG ZHENG. "2D/3D-QSAR STUDY ON ANALOGUES OF 2-METHOXYESTRADIOL WITH ANTICANCER ACTIVITY". Journal of Theoretical and Computational Chemistry 07, nr 02 (kwiecień 2008): 287–301. http://dx.doi.org/10.1142/s0219633608003745.
Pełny tekst źródłaRozprawy doktorskie na temat "QSAR Model"
Spreafico, Morena. "Mixed-model QSAR at the glucocorticoid and liver X receptors /". [S.l.] : [s.n.], 2009. http://edoc.unibas.ch/diss/DissB_8730.
Pełny tekst źródłaBagchi, Bhaskar. "Quantum chemical calculation and structure activity relationship of bioactive terpenoids". Thesis, University of North Bengal, 2016. http://ir.nbu.ac.in/handle/123456789/2762.
Pełny tekst źródłaRaynaud, Isabelle. "Etude des relations structure-activité quantitatives (QSAR) des cytokinines : synthèse et activité biologique de nouvelles molécules actives". Angers, 1996. http://www.theses.fr/1996ANGE0022.
Pełny tekst źródłaMazzatorta, Paolo. "Evaluation of pesticide toxicity : a hierarchical QSAR approach to model the acute aquatic toxicity and avian oral toxicity of pesticides". Thesis, Open University, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.424819.
Pełny tekst źródłaMalazizi, Ladan. "Development of Artificial Intelligence-based In-Silico Toxicity Models. Data Quality Analysis and Model Performance Enhancement through Data Generation". Thesis, University of Bradford, 2008. http://hdl.handle.net/10454/4262.
Pełny tekst źródłaModa, Tiago Luiz. "Desenvolvimento de modelos in silico de propriedades de ADME para a triagem de novos candidatos a fármacos". Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/76/76132/tde-22032007-112055/.
Pełny tekst źródłaMolecular modeling tools and quantitative structure-activity relantionships (QSAR) or structure-property (QSPR) are integrated into the drug design process in the search for new bioactive molecules with good pharmacokinetic and pharmacodynamic properties. The Medicinal Chemistry work carried out in this Masters dissertation concerned studies of the quantitative relationshisps between chemical structure and the pharmacokinetic properties oral bioavailability and plasma protein binding. In the present work, standard data sets for bioavailability and plasma protein binding were organized encompassing the structural information and corresponding pharmacokinetic data. The created data sets established the scientific basis for the development of predictive models using the hologram QSAR and VolSurf methods. The final HQSAR and VolSurf models posses high internal and external consistency with good correlative and predictive power. Due to the simplicity, robustness and effectivess, these models are useful guides in Medicinal Chemistry in the early stages of the drug discovery and development process.
MANSOURI, KAMEL. "New molecular descriptors for estimating degradation and fate of organic pollutants by QSAR/QSPR models within reach". Doctoral thesis, Università degli Studi di Milano-Bicocca, 2013. http://hdl.handle.net/10281/45611.
Pełny tekst źródłaDimitriadis, Spyridon. "Multi-task regression QSAR/QSPR prediction utilizing text-based Transformer Neural Network and single-task using feature-based models". Thesis, Linköpings universitet, Statistik och maskininlärning, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177186.
Pełny tekst źródłaSköld, Christian. "Computational Modeling of the AT2 Receptor and AT2 Receptor Ligands : Investigating Ligand Binding, Structure–Activity Relationships, and Receptor-Bound Models". Doctoral thesis, Uppsala University, Organic Pharmaceutical Chemistry, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7823.
Pełny tekst źródłaRational conversion of biologically active peptides to nonpeptide compounds with retained activity is an appealing approach in drug development. One important objective of the work presented in this thesis was to use computational modeling to aid in such a conversion of the peptide angiotensin II (Ang II, Asp-Arg-Val-Tyr-Ile-His-Pro-Phe). An equally important objective was to gain an understanding of the requirements for ligand binding to the Ang II receptors, with a focus on interactions with the AT2 receptor.
The bioactive conformation of a peptide can provide important guidance in peptidomimetic design. By designing and introducing well-defined secondary structure mimetics into Ang II the bioactive conformation can be addressed. In this work, both γ- and β-turn mimetic scaffolds have been designed and characterized for incorporation into Ang II. Using conformational analysis and the pharmacophore recognition method DISCO, a model was derived of the binding mode of the pseudopeptide Ang II analogues. This model indicated that the positioning of the Arg side chain was important for AT2 receptor binding, which was also supported when the structure–activity relationship of Ang II was investigated by performing a glycine scan.
To further examine ligand binding, a 3D model of the AT2 receptor was constructed employing homology modeling. Using this receptor model in a docking study of the ligands, binding modes were identified that were in agreement with data from point-mutation studies of the AT2 receptor.
By investigating truncated Ang II analogues, small pseudopeptides were developed that were structurally similar to nonpeptide AT2 receptor ligands. For further guidance in ligand design of nonpeptide compounds, three-dimensional quantitative structure–activity relationship models for AT1 and AT2 receptor affinity as well as selectivity were derived.
Sköld, Christian. "Computational Modeling of the AT2 Receptor and AT2 Receptor Ligands : Investigating Ligand Binding, Structure–Activity Relationships, and Receptor-Bound Models". Doctoral thesis, Uppsala universitet, Avdelningen för organisk farmaceutisk kemi, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-7823.
Pełny tekst źródłaKsiążki na temat "QSAR Model"
Romualdo, Benigni, red. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. Boca Raton, Fla: CRC Press, 2003.
Znajdź pełny tekst źródłaname, No. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. Boca Raton, FL: CRC Press, 2002.
Znajdź pełny tekst źródłaGonzález-Díaz, Humberto. Alignment-free models in plant genomics: Theoretical, experimental and legal issues. New York: Nova Science, 2010.
Znajdź pełny tekst źródłaKnaak, James B., Charles Timchalk i Rogelio Tornero-Velez, red. Parameters for Pesticide QSAR and PBPK/PD Models for Human Risk Assessment. Washington, DC: American Chemical Society, 2012. http://dx.doi.org/10.1021/bk-2012-1099.
Pełny tekst źródłaKnaak, James B., Charles Timchalk i Rogelio Tornero-Velez. Parameters for pesticide QSAR and PBPK/PD models for human risk assessment. Redaktorzy American Chemical Society i American Chemical Society. Division of Agrochemicals. Washington, DC: American Chemical Society, 2012.
Znajdź pełny tekst źródłaPrakash, Gupta Satya, i Bahal R, red. QSAR and molecular modeling studies in heterocyclic drugs. Berlin: Springer-Verlag, 2006.
Znajdź pełny tekst źródłaauthor, Panaye Annick, red. Three dimensional QSAR: Applications in pharmacology and toxicology. Boca Raton: CRC Press, 2010.
Znajdź pełny tekst źródła1944-, Truhlar Donald G., red. Rational drug design. New York: Springer, 1999.
Znajdź pełny tekst źródłaMartin, Yvonne Connolly. Quantitative drug design: A critical introduction. Wyd. 2. Boca Raton, FL: Taylor & Francis, 2010.
Znajdź pełny tekst źródłaMartin, Yvonne Connolly. Quantitative drug design: A critical introduction. Wyd. 2. Boca Raton: CRC Press/Taylor & Francis, 2010.
Znajdź pełny tekst źródłaCzęści książek na temat "QSAR Model"
Johansson, Erik, Lennart Eriksson, Maria Sandberg i Svante Wold. "QSAR Model Validation". W Molecular Modeling and Prediction of Bioactivity, 271–72. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4141-7_36.
Pełny tekst źródłaGrunewald, Gary L., Niels Skjaerbaek i James A. Monn. "An active site model of phenylethanolamine N-methyltransferase using CoMFA". W Trends in QSAR and Molecular Modelling 92, 513–16. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_138.
Pełny tekst źródłaSanz, F., E. López de Briñas, J. Rodríguez i F. Manaut. "Theoretical model for the metabolism of caffeine and its inhibition". W Trends in QSAR and Molecular Modelling 92, 193–96. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_29.
Pełny tekst źródłaVrontaki, Eleni, i Antonios Kolocouris. "Pharmacophore Generation and 3D-QSAR Model Development Using PHASE". W Methods in Molecular Biology, 387–401. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8630-9_23.
Pełny tekst źródłaRothe, H., i S. Boudon. "An approach to knowledge-based QSAR predictions using the MASCA model". W Trends in QSAR and Molecular Modelling 92, 502–3. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_134.
Pełny tekst źródłaOpreaa, Tudor Ionel, Ludovic Kurunczi i Eduard Eli Moret. "Role of the dipole moment during ligand receptor interaction: A hypothetic static model". W Trends in QSAR and Molecular Modelling 92, 398–99. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_92.
Pełny tekst źródłaHerbette, Leo G. "A structural model for drug interactions with biological membranes: Hydrophobicity, hydrophilicity and amphiphilicity in drug structures". W Trends in QSAR and Molecular Modelling 92, 76–85. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_10.
Pełny tekst źródłaBlankley, C. John, i Andrew D. White. "Lipophilic and electronic factors influencing the activity of a series of urea ACAT inhibitors: Approaches to model specification". W Trends in QSAR and Molecular Modelling 92, 349–51. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_73.
Pełny tekst źródłaKemmritz, Kerstin, i Hans-Dieter Höltje. "Theoretical investigations on the interaction of non-steroidal antiphlogistics with a model of the active site of the human prostaglandin endoperoxide synthase (‘cyclooxygenase’)". W Trends in QSAR and Molecular Modelling 92, 476–77. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-1472-1_124.
Pełny tekst źródłaGolani, Mati, i Idit I. Golani. "Neural Network Ensemble Based QSAR Model for the BBB Challenge: A Review". W Transactions on Engineering Technologies, 55–68. Dordrecht: Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7236-5_5.
Pełny tekst źródłaStreszczenia konferencji na temat "QSAR Model"
Boboriko, Natalia, He Liying i Yaraslau Dzichenka. "THE EXPLORATION OF CYP17A1 LIGAND SPACE BY THE QSAR MODEL". W 1st INTERNATIONAL Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2021. http://dx.doi.org/10.46793/iccbi21.439b.
Pełny tekst źródłaMunjal, Nupur S., Narendra Kumar, Manu Sharma i Chittaranjan Rout. "QSAR model development for solubility prediction of Paclitaxel". W 2016 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2016. http://dx.doi.org/10.1109/bsb.2016.7552139.
Pełny tekst źródłaDouali, Latifa. "QSAR model of phenols generated by deep neural network". W 2020 1st International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2020. http://dx.doi.org/10.1109/iraset48871.2020.9092000.
Pełny tekst źródłaDjokovic, Nemanja, Ana Postolovic i Katarina Nikolic. "MOLECULAR MODELING OF 5‐[(AMIDOBENZYL)OXY]‐ NICOTINAMIDES AS SIRTUIN 2 INHIBITORS USING ALIGNMENT- (IN)DEPENDENT 3D-QSAR ANALYSIS AND MOLECULAR DOCKING". W 1st INTERNATIONAL Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2021. http://dx.doi.org/10.46793/iccbi21.410dj.
Pełny tekst źródłaRagno, Rino, i Alessio Ragno. "db.3d-qsar.com. The first 3D QSAR models database". W 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.051r.
Pełny tekst źródła"Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties". W Sustainable Processes and Clean Energy Transition. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902516-27.
Pełny tekst źródłaConcu, Riccardo, i Maria Natalia Dias Soeiro Cordeiro. "A novel QSAR model to predict epidermial growth factor inhibitors". W MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition. Basel, Switzerland: MDPI, 2018. http://dx.doi.org/10.3390/mol2net-04-05261.
Pełny tekst źródłaUlfa, Adawiyah, Alhadi Bustamam, Arry Yanuar, Rizka Amalia i Prasnurzaki Anki. "Model QSAR Classification Using Conv1D-LSTM of Dipeptidyl Peptidase-4 Inhibitors". W 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE, 2021. http://dx.doi.org/10.1109/aims52415.2021.9466083.
Pełny tekst źródłaWang, Dan, Junjie Wang, Chaochao Yang i Yongqiang Ren. "Simulating QSAR Model of ERa Bioactivity by Statistics and Machine Learning". W ACM ICEA '21: 2021 ACM International Conference on Intelligent Computing and its Emerging Applications. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3491396.3506514.
Pełny tekst źródłaLevovnik, Bojan D., Aleksa P. Alargić, Miloš M. Svirčev i Goran I. Benedeković. "Building a 3D QSAR model with isopropylidene analogs of cytotoxic styryl-lactones". W 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.559l.
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