Littérature scientifique sur le sujet « QSAR Model »
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Articles de revues sur le sujet "QSAR Model"
Li, Yan Kun, et Xiao Ying Ma. « QSAR/QSPR Model Research of Complicated Samples ». Advanced Materials Research 740 (août 2013) : 306–9. http://dx.doi.org/10.4028/www.scientific.net/amr.740.306.
Texte intégralOkey, Robert W., et H. David Stensel. « A QSAR-based biodegradability model—A QSBR ». Water Research 30, no 9 (septembre 1996) : 2206–14. http://dx.doi.org/10.1016/0043-1354(96)00098-x.
Texte intégralZhang, Xiujun, H. G. Govardhana Reddy, Arcot Usha, M. C. Shanmukha, Mohammad Reza Farahani et Mehdi Alaeiyan. « A study on anti-malaria drugs using degree-based topological indices through QSPR analysis ». Mathematical Biosciences and Engineering 20, no 2 (2022) : 3594–609. http://dx.doi.org/10.3934/mbe.2023167.
Texte intégralToropov, Andrey A., et Alla P. Toropova. « The Monte Carlo Method as a Tool to Build up Predictive QSPR/QSAR ». Current Computer-Aided Drug Design 16, no 3 (2 juin 2020) : 197–206. http://dx.doi.org/10.2174/1573409915666190328123112.
Texte intégralMudasir, Mudasir, Iqmal Tahir et Ida Puji Astuti Maryono Putri. « QUANTITATIVE STRUCTURE AND ACTIVITY RELATIONSHIP ANALYSIS OF 1,2,4-THIADIAZOLINE FUNGICIDES BASED ON MOLECULAR STRUCTURE CALCULATED BY AM1 METHOD ». Indonesian Journal of Chemistry 3, no 1 (7 juin 2010) : 39–47. http://dx.doi.org/10.22146/ijc.21904.
Texte intégralSarkar, Bikash Kumar. « DFT Based QSAR Studies of Phenyl Triazolinones of Protoporphyrinogen Oxidase Inhibitors ». Asian Journal of Organic & ; Medicinal Chemistry 5, no 4 (31 décembre 2020) : 307–11. http://dx.doi.org/10.14233/ajomc.2020.ajomc-p280.
Texte intégralRybińska-Fryca, Anna, Anita Sosnowska et Tomasz Puzyn. « Representation of the Structure—A Key Point of Building QSAR/QSPR Models for Ionic Liquids ». Materials 13, no 11 (30 mai 2020) : 2500. http://dx.doi.org/10.3390/ma13112500.
Texte intégralPokle, Maithili S., Rashmi D. Singh et Madhura P. Vaidya. « 2D QSAR MODEL BASED ON 1,2-DISUBSTITUTED BENZIMIDAZOLES IMPDH INHIBITORS ». Indian Drugs 59, no 04 (1 juin 2022) : 18–23. http://dx.doi.org/10.53879/id.59.04.13117.
Texte intégralBu, Qingwei, Qingshan Li, Yun Liu et 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 octobre 2021) : 1–8. http://dx.doi.org/10.1155/2021/5563066.
Texte intégralLIAO, SI YAN, LI QIAN, JIN CAN CHEN, YONG SHEN et KANG CHENG ZHENG. « 2D/3D-QSAR STUDY ON ANALOGUES OF 2-METHOXYESTRADIOL WITH ANTICANCER ACTIVITY ». Journal of Theoretical and Computational Chemistry 07, no 02 (avril 2008) : 287–301. http://dx.doi.org/10.1142/s0219633608003745.
Texte intégralThèses sur le sujet "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.
Texte intégralBagchi, 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.
Texte intégralRaynaud, 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.
Texte intégralMazzatorta, 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.
Texte intégralMalazizi, 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.
Texte intégralModa, 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/.
Texte intégralMolecular 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.
Texte intégralDimitriadis, 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.
Texte intégralSköld, 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.
Texte intégralRational 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.
Texte intégralLivres sur le sujet "QSAR Model"
Romualdo, Benigni, dir. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. Boca Raton, Fla : CRC Press, 2003.
Trouver le texte intégralname, No. Quantitative structure-activity relationship (QSAR) models of mutagens and carcinogens. Boca Raton, FL : CRC Press, 2002.
Trouver le texte intégralGonzález-Díaz, Humberto. Alignment-free models in plant genomics : Theoretical, experimental and legal issues. New York : Nova Science, 2010.
Trouver le texte intégralKnaak, James B., Charles Timchalk et Rogelio Tornero-Velez, dir. 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.
Texte intégralKnaak, James B., Charles Timchalk et Rogelio Tornero-Velez. Parameters for pesticide QSAR and PBPK/PD models for human risk assessment. Sous la direction de American Chemical Society et American Chemical Society. Division of Agrochemicals. Washington, DC : American Chemical Society, 2012.
Trouver le texte intégralPrakash, Gupta Satya, et Bahal R, dir. QSAR and molecular modeling studies in heterocyclic drugs. Berlin : Springer-Verlag, 2006.
Trouver le texte intégralauthor, Panaye Annick, dir. Three dimensional QSAR : Applications in pharmacology and toxicology. Boca Raton : CRC Press, 2010.
Trouver le texte intégral1944-, Truhlar Donald G., dir. Rational drug design. New York : Springer, 1999.
Trouver le texte intégralMartin, Yvonne Connolly. Quantitative drug design : A critical introduction. 2e éd. Boca Raton, FL : Taylor & Francis, 2010.
Trouver le texte intégralMartin, Yvonne Connolly. Quantitative drug design : A critical introduction. 2e éd. Boca Raton : CRC Press/Taylor & Francis, 2010.
Trouver le texte intégralChapitres de livres sur le sujet "QSAR Model"
Johansson, Erik, Lennart Eriksson, Maria Sandberg et Svante Wold. « QSAR Model Validation ». Dans 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.
Texte intégralGrunewald, Gary L., Niels Skjaerbaek et James A. Monn. « An active site model of phenylethanolamine N-methyltransferase using CoMFA ». Dans 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.
Texte intégralSanz, F., E. López de Briñas, J. Rodríguez et F. Manaut. « Theoretical model for the metabolism of caffeine and its inhibition ». Dans 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.
Texte intégralVrontaki, Eleni, et Antonios Kolocouris. « Pharmacophore Generation and 3D-QSAR Model Development Using PHASE ». Dans 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.
Texte intégralRothe, H., et S. Boudon. « An approach to knowledge-based QSAR predictions using the MASCA model ». Dans 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.
Texte intégralOpreaa, Tudor Ionel, Ludovic Kurunczi et Eduard Eli Moret. « Role of the dipole moment during ligand receptor interaction : A hypothetic static model ». Dans 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.
Texte intégralHerbette, Leo G. « A structural model for drug interactions with biological membranes : Hydrophobicity, hydrophilicity and amphiphilicity in drug structures ». Dans 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.
Texte intégralBlankley, C. John, et Andrew D. White. « Lipophilic and electronic factors influencing the activity of a series of urea ACAT inhibitors : Approaches to model specification ». Dans 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.
Texte intégralKemmritz, Kerstin, et 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’) ». Dans 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.
Texte intégralGolani, Mati, et Idit I. Golani. « Neural Network Ensemble Based QSAR Model for the BBB Challenge : A Review ». Dans Transactions on Engineering Technologies, 55–68. Dordrecht : Springer Netherlands, 2015. http://dx.doi.org/10.1007/978-94-017-7236-5_5.
Texte intégralActes de conférences sur le sujet "QSAR Model"
Boboriko, Natalia, He Liying et Yaraslau Dzichenka. « THE EXPLORATION OF CYP17A1 LIGAND SPACE BY THE QSAR MODEL ». Dans 1st INTERNATIONAL Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2021. http://dx.doi.org/10.46793/iccbi21.439b.
Texte intégralMunjal, Nupur S., Narendra Kumar, Manu Sharma et Chittaranjan Rout. « QSAR model development for solubility prediction of Paclitaxel ». Dans 2016 International Conference on Bioinformatics and Systems Biology (BSB). IEEE, 2016. http://dx.doi.org/10.1109/bsb.2016.7552139.
Texte intégralDouali, Latifa. « QSAR model of phenols generated by deep neural network ». Dans 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.
Texte intégralDjokovic, Nemanja, Ana Postolovic et Katarina Nikolic. « MOLECULAR MODELING OF 5‐[(AMIDOBENZYL)OXY]‐ NICOTINAMIDES AS SIRTUIN 2 INHIBITORS USING ALIGNMENT- (IN)DEPENDENT 3D-QSAR ANALYSIS AND MOLECULAR DOCKING ». Dans 1st INTERNATIONAL Conference on Chemo and BioInformatics. Institute for Information Technologies, University of Kragujevac, 2021. http://dx.doi.org/10.46793/iccbi21.410dj.
Texte intégralRagno, Rino, et Alessio Ragno. « db.3d-qsar.com. The first 3D QSAR models database ». Dans 2nd International Conference on Chemo and Bioinformatics. Institute for Information Technologies, University of Kragujevac, 2023. http://dx.doi.org/10.46793/iccbi23.051r.
Texte intégral« Application of machine learning models to predict ecotoxicity of ionic liquids (Vibrio fischeri) using VolSurf principal properties ». Dans Sustainable Processes and Clean Energy Transition. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902516-27.
Texte intégralConcu, Riccardo, et Maria Natalia Dias Soeiro Cordeiro. « A novel QSAR model to predict epidermial growth factor inhibitors ». Dans MOL2NET 2018, International Conference on Multidisciplinary Sciences, 4th edition. Basel, Switzerland : MDPI, 2018. http://dx.doi.org/10.3390/mol2net-04-05261.
Texte intégralUlfa, Adawiyah, Alhadi Bustamam, Arry Yanuar, Rizka Amalia et Prasnurzaki Anki. « Model QSAR Classification Using Conv1D-LSTM of Dipeptidyl Peptidase-4 Inhibitors ». Dans 2021 International Conference on Artificial Intelligence and Mechatronics Systems (AIMS). IEEE, 2021. http://dx.doi.org/10.1109/aims52415.2021.9466083.
Texte intégralWang, Dan, Junjie Wang, Chaochao Yang et Yongqiang Ren. « Simulating QSAR Model of ERa Bioactivity by Statistics and Machine Learning ». Dans 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.
Texte intégralLevovnik, Bojan D., Aleksa P. Alargić, Miloš M. Svirčev et Goran I. Benedeković. « Building a 3D QSAR model with isopropylidene analogs of cytotoxic styryl-lactones ». Dans 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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