Littérature scientifique sur le sujet « Multi-target drug »
Créez une référence correcte selon les styles APA, MLA, Chicago, Harvard et plusieurs autres
Sommaire
Consultez les listes thématiques d’articles de revues, de livres, de thèses, de rapports de conférences et d’autres sources académiques sur le sujet « Multi-target drug ».
À côté de chaque source dans la liste de références il y a un bouton « Ajouter à la bibliographie ». Cliquez sur ce bouton, et nous générerons automatiquement la référence bibliographique pour la source choisie selon votre style de citation préféré : APA, MLA, Harvard, Vancouver, Chicago, etc.
Vous pouvez aussi télécharger le texte intégral de la publication scolaire au format pdf et consulter son résumé en ligne lorsque ces informations sont inclues dans les métadonnées.
Articles de revues sur le sujet "Multi-target drug"
Kondej, Magda, Piotr Stępnicki et Agnieszka A. Kaczor. « Multi-Target Approach for Drug Discovery against Schizophrenia ». International Journal of Molecular Sciences 19, no 10 (10 octobre 2018) : 3105. http://dx.doi.org/10.3390/ijms19103105.
Texte intégralde Oliveira Viana, Jessika, Hamilton Mitsugu Ishiki, Marcus Tullius Scotti et Luciana Scotti. « Multi-Target Antitubercular Drugs ». Current Topics in Medicinal Chemistry 18, no 9 (31 juillet 2018) : 750–58. http://dx.doi.org/10.2174/1568026618666180528124414.
Texte intégralJaiswal, Varun. « Multi target drug design for gastrointestinal cancer ». Annals of Oncology 28 (juin 2017) : iii18—iii19. http://dx.doi.org/10.1093/annonc/mdx261.020.
Texte intégralLu, Jin-Jian, Wei Pan, Yuan-Jia Hu et Yi-Tao Wang. « Multi-Target Drugs : The Trend of Drug Research and Development ». PLoS ONE 7, no 6 (29 juin 2012) : e40262. http://dx.doi.org/10.1371/journal.pone.0040262.
Texte intégralMei, Suyu, et Kun Zhang. « A Multi-Label Learning Framework for Drug Repurposing ». Pharmaceutics 11, no 9 (9 septembre 2019) : 466. http://dx.doi.org/10.3390/pharmaceutics11090466.
Texte intégralBraga, Susana Santos. « Multi-target drugs active against leishmaniasis : A paradigm of drug repurposing ». European Journal of Medicinal Chemistry 183 (décembre 2019) : 111660. http://dx.doi.org/10.1016/j.ejmech.2019.111660.
Texte intégralZanni, Riccardo, María Galvez-Llompart, Jorge Galvez et Ramon García-Domenech. « QSAR Multi-Target in Drug Discovery : A Review ». Current Computer Aided-Drug Design 10, no 2 (31 juillet 2014) : 129–36. http://dx.doi.org/10.2174/157340991002140708105124.
Texte intégralPeng, Lihong, Bo Liao, Wen Zhu, Zejun Li et Keqin Li. « Predicting Drug–Target Interactions With Multi-Information Fusion ». IEEE Journal of Biomedical and Health Informatics 21, no 2 (mars 2017) : 561–72. http://dx.doi.org/10.1109/jbhi.2015.2513200.
Texte intégralMa, Xiao Hua, Zhe Shi, Chunyan Tan, Yuyang Jiang, Mei Lin Go, Boon Chuan Low et Yu Zong Chen. « In-Silico Approaches to Multi-target Drug Discovery ». Pharmaceutical Research 27, no 5 (11 mars 2010) : 739–49. http://dx.doi.org/10.1007/s11095-010-0065-2.
Texte intégralLiu, X., F. Zhu, X. H. Ma, Z. Shi, S. Y. Yang, Y. Q. Wei et Y. Z. Chen. « Predicting Targeted Polypharmacology for Drug Repositioning and Multi- Target Drug Discovery ». Current Medicinal Chemistry 20, no 13 (1 mars 2013) : 1646–61. http://dx.doi.org/10.2174/0929867311320130005.
Texte intégralThèses sur le sujet "Multi-target drug"
Pérez, Areales Francisco Javier. « Novel multi-target directed ligands as drug candidates against Alzheimer’s disease ». Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/404781.
Texte intégralKoptelov, Maksim. « Link prediction in bipartite multi-layer networks, with an application to drug-target interaction prediction ». Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC211.
Texte intégralMany aspects from real life with bi-relational structure can be modeled as bipartite networks. This modeling allows the use of some standard solutions for prediction and/or recommendation of new relations between these objects in such networks. Known as the link prediction task, it is a widely studied problem in network science for single graphs, networks assuming one type of interaction between vertices. For multi-layer networks, allowing more than one type of edges between vertices, the problem is not yet fully solved.The motivation of this thesis comes from the importance of an application task, drug-target interaction prediction. Searching valid drug candidates for a given biological target is an essential part of modern drug development. In this thesis, the problem is modeled as link prediction in a bipartite multi-layer network. Modeling the problem in this setting helps to aggregate different sources of information into one single structure and as a result to improve the quality of link prediction.The thesis mostly focuses on the problem of link prediction in bipartite multi-layer networks and makes two main contributions on this topic.The first contribution provides a solution for solving link prediction in the given setting without limiting the number and type of networks, the main constrains of the state of the art methods. Modeling random walk in the fashion of PageRank, the algorithm that we developed is able to predict new interactions in the network constructed from different sources of information. The second contribution, which solves link prediction using community information, is less straight-forward and more dependent on fixing the parameters, but provides better results. Adopting existing community measures for link prediction to the case of bipartite multi-layer networks and proposing alternative ways for exploiting communities, the method offers better performance and efficiency. Additional evaluation on the data of a different origin than drug-target interactions demonstrate the genericness of proposed approach.In addition to the developed approaches, we propose a framework for validation of predicted interactions founded on an external resource. Based on a collection of biomedical concepts used as a knowledge source, the framework is able to perform validation of drug-target pairs using proposed confidence scores. An evaluation of predicted interactions performed on unseen data shows effectiveness of this framework.At the end, a problem of identification and characterization of promiscuous compounds existing in the drug development process is discussed. The problem is solved as a machine learning classification task. The contribution includes graph mining and sampling approaches. In addition, a graphical interface was developed to provide feedback of the result for experts
Di, Pietro Ornella. « Exploring heterocyclic scaffolds in the development of multi-target anti-Alzheimer and multi-trypanosomatid compounds ». Doctoral thesis, Universitat de Barcelona, 2015. http://hdl.handle.net/10803/318585.
Texte intégralHerman, Jonathan David. « Halofuginone : A Story of How Target Identification of an Ancient Chinese Medicine and Multi-Step Evolution Informs Malaria Drug Discovery ». Thesis, Harvard University, 2014. http://dissertations.umi.com/gsas.harvard:11540.
Texte intégralLoguercio, Salvatore. « Reductionist and Integrative approaches to explore the H.pylori genome ». Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3425099.
Texte intégralDalla, Via Martina. « Development of multifunctional anticancer agents : design, synthesis and evaluation of hybrid compounds containing kinase inhibitor moieties ». Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3421807.
Texte intégralIl cancro è una patologia complessa che coinvolge più geni; per questo motivo non può essere trattato o curato con un singolo farmaco che regola l'attività biologica di un unico bersaglio. L'innovazione dei farmaci multi-target, che combinano l'attività contro diversi bersagli coinvolti nella progressione del tumore, è diventato un promettente argomento di ricerca. Farmaci che agiscono su più bersagli possono aumentare l'efficacia della terapia riducendo il fenomeno di resistenza che causa ricadute e metastasi restando uno dei maggiori ostacoli della terapia antitumorale. Le tirosin-chinasi sono considerate ad oggi tra i principali bersagli in quanto molte protein chinasi stimolando la crescita, la proliferazione e la migrazione cellulare e se sovra espresse, amplificate o costitutivamente attivate assumono proprietà oncogeniche. Altri bersagli biologici ideali sono enzimi quali gli istone deacetilasi e le funzioni mitocondriali. Nella tesi sono presentati lo sviluppo e la valutazione biologica preliminare di nuovi inibitori duali di Abl e HDAC caratterizzati da una porzione pirido-pirimidinica; la funzionalizzazione dei composti più attivi con ioni metallici (i.e. Zn2+, Cu2+ and Fe3+); lo sviluppo di nuovi inibitori multi-chinasi caratterizzati da una porzione 4-anilinopirimidinica; lo sviluppo di nuovi inibitori di cKIT/wtRET/V804MRET a struttura 4-anilinopiridinica. Sono inoltre riportati lo sviluppo di nuovi inibitori multichinasici ad attività antifibrotica ed inibitori di topoisomerasi.
Teponnou, Gerard A. Kenfack. « Tacrine, trolox and tryptoline as lead compounds for the design and synthesis of multi-target drugs for Alzheimer's disease therapy ». Thesis, University of the Western Cape, 2016. http://hdl.handle.net/11394/5344.
Texte intégralThe cascade of neurotoxic events involved in the pathogenesis of Alzheimer's disease may explain the inefficacy of currently available treatment based on acetylcholinesterase inhibitors (AChEI - donepezil, galantamine, rivastigmine) and N-methyl-D-aspartate (NMDA) antagonists (memantine). These drugs were designed based on the "one-moleculeone- target" paradigm and only address a single target. Conversely, the multi-target drug design strategy increasingly gains recognition. Based on the versatile biological activities of tacrine, trolox and β-carboline derivatives, the attention they have received as lead structures for the design of multifunctional drugs for the treatment of Alzheimer's disease, and the topology of the active site of AChE, we have designed tacrine-trolox and tacrine-tryptoline hybrids with various linker chain lengths. The aim with these hybrids was to provide additive or synergistic therapeutic effects that might help overcome the limitation of current anti Alzheimer's disease drugs. All synthesized compounds were designed from lead structures (tacrine, tryptoline and trolox) to obtain cholinesterase (ChE) multisite binders and multifunctional AD agents. The study was rationalized by docking all structures in the active site of TcAChE using Molecular Operating Environment (MOE) software before proceeding with the synthesis. ChE inhibition was assessed in a UV enzyme inhibition assay using Ellman's method. Antioxidant activities were assessed using the 2, 2-diphenyl-1-picrylhydrazyl (DPPH.) absorbance assay. The hybrids containing the trolox moiety (compounds 8a-e) showed moderate to high AChE inhibitory activity in the nano to micro molar range (IC₅₀: 17.37 - 2200 nM), BuChE inhibition was observed in the same range (IC₅₀: 3.16 – 128.82 nM), and free radical scavenging activities in micro molar range (IC50: 11.48 – 49.23 µM). These are comparable or slightly higher than their reference compounds donepezil (AChE IC₅₀ = 220 nM), tacrine (BuChE IC₅₀: 14.12 nM), and trolox (DPPH IC₅₀: 17.57 µM). The hybrids with longer linker chain lengths, 6 and 8 carbons (8d and 8e), showed better ChE inhibitory activity than the shorter ones, 2, 3, and 4 carbons (8a-c respectively). This correlates well with literature. Free radical scavenging activities, however, seems not to be significantly affected by varying linker chain lengths. The hybrid compound (14) containing the tryptoline moiety linked with a 7 carbon spacer displayed the best AChE and BuChE inhibitory activity (IC₅₀ = 17.37 and 3.16 nM) but poor free radical scavenging activity. Novel anti-Alzheimer's disease drugs with multi-target neuroprotective activities were thus obtained and hybrid molecules that exhibit good ChE inhibition (8d, 8e and 14) and anti-oxidant (8d and 8e) activity were identified as suitable candidates for further investigation.
National Research Foundation (NRF)
Gencarelli, Manuela. « Revisiting targets for HCN blockers in the heart and urinary bladder : evidence for antimuscarinic activity in human atrial preparations, rat urinary bladder and recombinant muscarinic receptors ». Doctoral thesis, 2022. http://hdl.handle.net/2158/1280560.
Texte intégralLivres sur le sujet "Multi-target drug"
Roy, Kunal, dir. Multi-Target Drug Design Using Chem-Bioinformatic Approaches. New York, NY : Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-8733-7.
Texte intégralMorphy, J. Richard, et C. John Harris, dir. Designing Multi-Target Drugs. Cambridge : Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912.
Texte intégralDesigning Multi-Target Drugs. Royal Society of Chemistry, The, 2012.
Trouver le texte intégralMulti-Target Drug Design Using Chem-Bioinformatic Approaches. Humana, 2018.
Trouver le texte intégralHerman, Jonathan David. HALOFUGINONE : A STORY OF HOW TARGET IDENTIFICATION OF AN ANCIENT CHINESE MEDICINE AND MULTI-STEP EVOLUTION INFORMS MALARIA DRUG DISCOVERY. 2014.
Trouver le texte intégralKrueger, Darcy A., et Jamie Capal. Familial CNS Tumor Syndromes. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199937837.003.0136.
Texte intégralChapitres de livres sur le sujet "Multi-target drug"
Abdolmaleki, Azizeh, Fereshteh Shiri et Jahan B. Ghasemi. « Computational Multi-Target Drug Design ». Dans Methods in Pharmacology and Toxicology, 51–90. New York, NY : Springer New York, 2018. http://dx.doi.org/10.1007/7653_2018_23.
Texte intégralJayaraman, Prem kumar, Mohammad Imran Siddiqi, Meena K. Sakharkar, Ramesh Chandra et Kishore R. Sakharkar. « Hypothesis Driven Multi-target Drug Design ». Dans Post-genomic Approaches in Drug and Vaccine Development, 133–77. New York : River Publishers, 2022. http://dx.doi.org/10.1201/9781003339090-7.
Texte intégralRichard Morphy, J. « Chapter 10. The Challenges of Multi-Target Lead Optimization ». Dans Drug Discovery, 141–54. Cambridge : Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00141.
Texte intégralCai, Xiong, et Changgeng Qian. « Chapter 14. Discovery of HDAC-Inhibiting Multi-Target Inhibitors ». Dans Drug Discovery, 221–42. Cambridge : Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00221.
Texte intégralRankovic, Zoran, et Richard Morphy. « CHAPTER 19. Multi-target Drug Discovery for Psychiatric Disorders ». Dans Drug Discovery, 510–33. Cambridge : Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734943-00510.
Texte intégralGupta, Neelima, Prateek Pandya et Seema Verma. « Computational Predictions for Multi-Target Drug Design ». Dans Methods in Pharmacology and Toxicology, 27–50. New York, NY : Springer New York, 2018. http://dx.doi.org/10.1007/7653_2018_26.
Texte intégralShahid, Mohammed. « Chapter 2. Clinical Need and Rationale for Multi-Target Drugs in Psychiatry ». Dans Drug Discovery, 14–31. Cambridge : Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00014.
Texte intégralMason, Jonathan S. « Chapter 5. Designing Multi-Target Drugs : In Vitro Panel Screening – Biological Fingerprinting ». Dans Drug Discovery, 66–85. Cambridge : Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00066.
Texte intégralMa, Xiaohou, et Yuzong Chen. « Chapter 9. In Silico Lead Generation Approaches in Multi-Target Drug Discovery ». Dans Drug Discovery, 130–40. Cambridge : Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00130.
Texte intégralBolognesi, Maria Laura, Carlo Melchiorre, Cornelis J. Van der Schyf et Moussa Youdim. « Chapter 18. Discovery of Multi-Target Agents for Neurological Diseases via Ligand Design ». Dans Drug Discovery, 290–315. Cambridge : Royal Society of Chemistry, 2012. http://dx.doi.org/10.1039/9781849734912-00290.
Texte intégralActes de conférences sur le sujet "Multi-target drug"
Cai, Ruichu, Zhenjie Zhang, Srinivasan Parthasarathy, Anthony K. H. Tung, Zhifeng Hao et Wen Zhang. « Multi-Domain Manifold Learning for Drug-Target Interaction Prediction ». Dans Proceedings of the 2016 SIAM International Conference on Data Mining. Philadelphia, PA : Society for Industrial and Applied Mathematics, 2016. http://dx.doi.org/10.1137/1.9781611974348.3.
Texte intégralChen, Jiatao, Liang Zhang, Ke Cheng, Bo Jin, Xinjiang Lu, Chao Che et Yiwei Liu. « Exploring Multi-level Mutual Information for Drug-target Interaction Prediction ». Dans 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313395.
Texte intégralNishamol, P. H., et G. Gopakumar. « Multi-target drug discovery using system polypharmacology-state of the art ». Dans 2015 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES). IEEE, 2015. http://dx.doi.org/10.1109/spices.2015.7091430.
Texte intégralWeng, Yuyou, Chen Lin, Xiangxiang Zeng et Yun Liang. « Drug Target Interaction Prediction using Multi-task Learning and Co-attention ». Dans 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983254.
Texte intégralAdilova, Fatima, et Alisher Ikramov. « Using Support Vector Regression in multi-target prediction of drug toxicity ». Dans 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2020. http://dx.doi.org/10.1109/aict50176.2020.9368837.
Texte intégralMa, Xin, et Yujing Cheng. « Prediction of Drug-Target Interaction Based on Multi-Head Self-Attention ». Dans 2022 IEEE Conference on Telecommunications, Optics and Computer Science (TOCS). IEEE, 2022. http://dx.doi.org/10.1109/tocs56154.2022.10016085.
Texte intégralPillai, Suchitha Chandran, P. K. Krishnan Namboori, N. C. Anil Kumar, K. Varun Gopal, A. Suresh Kumar et P. Bharath. « Investigating multi target drug action of Aloe vera using computational analysis ». Dans 2011 2nd National Conference on Emerging Trends and Applications in Computer Science (NCETACS). IEEE, 2011. http://dx.doi.org/10.1109/ncetacs.2011.5751406.
Texte intégralFitriawan, Aries, Ito Wasito, Arida Ferti Syafiandini, Mukhlis Amien et Arry Yanuar. « Multi-label classification using deep belief networks for virtual screening of multi-target drug ». Dans 2016 International Conference on Computer, Control, Informatics and its Applications (IC3INA). IEEE, 2016. http://dx.doi.org/10.1109/ic3ina.2016.7863032.
Texte intégralSpolaor, Simone, Daniele M. Papetti, Paolo Cazzaniga, Daniela Besozzi et Marco S. Nobile. « A comparison of multi-objective optimization algorithms to identify drug target combinations ». Dans 2021 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2021. http://dx.doi.org/10.1109/cibcb49929.2021.9562773.
Texte intégralJin, Xu, MingMing Liu, Lin Wang, WenQian He, YaLou Huang et MaoQiang Xie. « Multi-Resolutional Collaborative Heterogeneous Graph Convolutional Auto-Encoder for Drug-Target Interaction Prediction ». Dans 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9313489.
Texte intégralRapports d'organisations sur le sujet "Multi-target drug"
Ayoul-Guilmard, Q., F. Nobile, S. Ganesh, M. Nuñez, R. Tosi, C. Soriano et R. Rosi. D5.5 Report on the application of multi-level Monte Carlo to wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.03.
Texte intégralJorgensen, Frieda, Andre Charlett, Craig Swift, Anais Painset et Nicolae Corcionivoschi. A survey of the levels of Campylobacter spp. contamination and prevalence of selected antimicrobial resistance determinants in fresh whole UK-produced chilled chickens at retail sale (non-major retailers). Food Standards Agency, juin 2021. http://dx.doi.org/10.46756/sci.fsa.xls618.
Texte intégral