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Статті в журналах з теми "SILICO SCREENING"
Recanatini, Maurizio, Giovanni Bottegoni, and Andrea Cavalli. "In silico antitarget screening." Drug Discovery Today: Technologies 1, no. 3 (December 2004): 209–15. http://dx.doi.org/10.1016/j.ddtec.2004.10.004.
Повний текст джерелаRudisser, Simon, and Wolfgang Jahnke. "NMR and In silico Screening." Combinatorial Chemistry & High Throughput Screening 5, no. 8 (December 1, 2002): 591–603. http://dx.doi.org/10.2174/1386207023329987.
Повний текст джерелаGallinger, Tom L., Samuel Y. Aboagye, Wiebke Obermann, Michael Weiss, Arnold Grünweller, Carlo Unverzagt, David L. Williams, Martin Schlitzer, and Simone Haeberlein. "First In Silico Screening of Insect Molecules for Identification of Novel Anti-Parasitic Compounds." Pharmaceuticals 15, no. 2 (January 19, 2022): 119. http://dx.doi.org/10.3390/ph15020119.
Повний текст джерелаSeifert, Markus H. J., Kristina Wolf, and Daniel Vitt. "Virtual high-throughput in silico screening." BIOSILICO 1, no. 4 (September 2003): 143–49. http://dx.doi.org/10.1016/s1478-5382(03)02359-x.
Повний текст джерелаReddy, Bandi Deepa, and Ch M. Kumari Chitturi. "Screening and Identification of Microbial Derivatives for Inhibiting Legumain: An In silico Approach." Journal of Pure and Applied Microbiology 12, no. 3 (September 30, 2018): 1623–30. http://dx.doi.org/10.22207/jpam.12.3.69.
Повний текст джерелаMa, Dik-Lung, Victor Pui-Yan Ma, Daniel Shiu-Hin Chan, Ka-Ho Leung, Hai-Jing Zhong, and Chung-Hang Leung. "In silico screening of quadruplex-binding ligands." Methods 57, no. 1 (May 2012): 106–14. http://dx.doi.org/10.1016/j.ymeth.2012.02.001.
Повний текст джерелаLin, Li-Chiang, Adam H. Berger, Richard L. Martin, Jihan Kim, Joseph A. Swisher, Kuldeep Jariwala, Chris H. Rycroft, et al. "In silico screening of carbon-capture materials." Nature Materials 11, no. 7 (May 27, 2012): 633–41. http://dx.doi.org/10.1038/nmat3336.
Повний текст джерелаArvidson, Kirk B., Luis G. Valerio, Marilyn Diaz, and Ronald F. Chanderbhan. "In Silico Toxicological Screening of Natural Products." Toxicology Mechanisms and Methods 18, no. 2-3 (January 2008): 229–42. http://dx.doi.org/10.1080/15376510701856991.
Повний текст джерелаVidal, David, and Jordi Mestres. "In Silico Receptorome Screening of Antipsychotic Drugs." Molecular Informatics 29, no. 6-7 (July 9, 2010): 543–51. http://dx.doi.org/10.1002/minf.201000055.
Повний текст джерелаZaini, Vikra Ardiansyah, Purwantiningsih Sugita, Luthfan Irfana та Suminar Setiati Achmadi. "In Silico Screening Anticancer of Six Triterpenoids toward miR-494 and TNF-α Targets". Jurnal Kimia Sains dan Aplikasi 23, № 4 (7 квітня 2020): 117–23. http://dx.doi.org/10.14710/jksa.23.4.117-123.
Повний текст джерелаДисертації з теми "SILICO SCREENING"
Dunkel, Mathias [Verfasser]. "3D Konformationsdatenbanken für das in silico Screening / Mathias Dunkel." Berlin : Medizinische Fakultät Charité - Universitätsmedizin Berlin, 2008. http://d-nb.info/1023262142/34.
Повний текст джерелаBarakat, Nora Hisham. "Combining in vivo and in silico screening for protein stability." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2007. http://wwwlib.umi.com/cr/ucsd/fullcit?p3258327.
Повний текст джерелаTitle from first page of PDF file (viewed May 29, 2007). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 137-152).
Füllbeck, Melanie. "In silico und in vitro Screening von Proteinliganden zur Apoptoseinduktion." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2007. http://dx.doi.org/10.18452/15702.
Повний текст джерелаNowadays, cancer research is focused on the overcoming of survival strategies of malign tumors. In the present work, computer-based methods lead to the identification of novel apoptosis inducing molecules, whose potency should be validated in in vitro experiments. Novel compounds, which induce apoptosis in cancer cells, could be identified on the basis of three projects. Inhibitors for the COP9 signalosome (CSN) associated kinases CK2 and PKD could be discovered using curcumin and emodin as lead compounds. Investigations concerning the mechanism-of-action of betulinic acid (BA) should give information about the function of the Bcl-2 protein family in the BA induced cell death. The experiments, which are focused on the mitochondrial signalling pathway, revealed that BA induces apoptosis in an almost independent manner with regards to the pro- and anti-apoptotic Bcl-2 proteins, but dependent on the presence of activated caspases. Via an in silico screening and the utilisation of a new property filter, novel BA analogues could be identified. For the first time, the data of the National Cancer Institute (NCI) is employed to evaluate results from the in silico screening. In the third project two novel Bcl-2 inhibitors have been identified via in silico screenings, docking experiments and in vitro screenings, which are performed at the moment. The insertion of a photo-switchable compound to the amino acid side chain of an alpha-helical peptide from the pro-apoptotic protein Bid triggered the effect of a modulator, which results in a controllable activation and initiation of apoptosis in tumor cells. In silico screenings as a reliable method corroborated that a systematical evaluation of the virtual hits could decrease the time and costs of experimental testings. The identified hits could serve as novel lead compounds for further in silico screenings or enter the next steps in the development of novel drugs using optimisation methods.
MONTE, M. LO. "IN SILICO SCREENING OF TASTE RECEPTORS: AN INTEGRATE MODELING APPROACH." Doctoral thesis, Università degli Studi di Milano, 2015. http://hdl.handle.net/2434/252746.
Повний текст джерелаHarding, Simon D. "Database analysis of protein-peptide interactions and in silico screening for peptidomimetics." Thesis, University of Edinburgh, 2008. http://hdl.handle.net/1842/10935.
Повний текст джерелаLauro, Gianluigi. "New techniques of molecular modelling and structural chemistry for the development of bioactive compounds." Doctoral thesis, Universita degli studi di Salerno, 2013. http://hdl.handle.net/10556/986.
Повний текст джерелаComputational chemistry represents today a valid and fast tool for the research of new compounds with potential biological activity. The analysis of ligand-macromolecule interactions and the evaluation of possible “binding modes” have a crucial role for the design and the development of new and more powerful drugs. In silico Virtual Screening campaigns of large libraries compounds (fragments or drug-like) on a specific target allow the selection of promising compounds, leading the identification of new scaffolds. The accurate analysis and the comparison of different bioactive compounds clarify the molecular basis of their interaction and the construction of pharmacoforic models. In parallel, another crucial aspect of pharmacological research is the identification of targets of interaction of bioactive molecules, and this is particularly true for compounds from natural sources. In fact, a wide range of drug tests on a large number of biological targets can represent a useful approach for the study of natural products, but often one of the main problems is their limited availability. Starting from these assumptions, a new computational method named Inverse Virtual Screening is described in details in this thesis. The different works based on this approach were performed considering panels of targets involved in the cancer events, determining the identification of the specific antitumor activity of the natural compounds investigated. Inverse Virtual Screening studies were performed by means of molecular docking experiments on different natural compounds, organized in small libraries or as single compounds. Firstly, a mathematical method for the exclusion of false positive and false negative results was proposed applying a normalization of the predicted binding energies (expressed in kcal/mol) obtained from the docking calculations. Then this approach was applied on a library of 10 compounds extracted from natural sources, obtaining a good validation through in vitro biological tests. Afterwards, another study was performed on the cyclopeptide namalide. Its biological inhibitory activity and selectivity on Carboxipeptidase A target was in accordance with Inverse Virtual Screening results. Virtual Screening topic was also inspected analyzing the efficacy of Molecular Dynamics-based methods for the accurate calculations of the binding affinities. This work was conducted on a library of 1588 compounds (44 ligands + 1544 decoys) extracted from the DUD database on trypsin target, using the Linear Interaction Energy (LIE) method by means of extensive Molecular Dynamics simulations. Four different LIE results obtained combining different scaling factors were compared with docking results, evaluating and comparing ROC and enrichment curves for each of the considered methods. Poor results were obtained with LIE, and further analysis with MM-GBSA and MM-PBSA approaches are under investigation. Moreover, in silico screenings were performed for the detailed study of natural compounds whose activities are known a priori. With this procedure, several binding modes were reported for a library of compounds on PXR target, whose activity or inactivity were rationalized comparing their binding poses with that of Solomonsterol A, used as a reference compound on this receptor. The presence/absence of biological activity of another library of compounds extracted from the marine sponge Plakinastrella Mamillaris on PPAR-γ and for the diterpene oridonin on HSP70 1A are described at a molecular level with molecular docking and Molecular Dynamics simulations. The putative binding modes for the reported molecules was described offering a complete rationalization of docking results, evaluating how ligand target specific interactions (e.g. hydrophobic, hydrophilic, electrostatic contacts) can influence their biological activity. [edited by author]
La chimica computazionale rappresenata un valido e rapido strumento per l’identificazione di nuovi potenziali composti bioattivi. L’analisi delle interazioni ligando-target macromolecolare e la valutazione di un possibile “binding mode” sono cruciali per il design e lo sviluppo di nuovi potenziali farmaci. Il Virtual Screening di grandi librerie di composti (fragments o drug-like) condotto in silico su uno specifico recettore può permettere la selezione di composti dalla promettente attività, e parallelamente l’identificazione di nuovi scaffolds molecolari. L’analisi accurata dei modelli di interazione ligando-recettore e il confronto di tali modelli con quelli di composti dalla già nota attività permette la costruzione di un modello farmacoforico, punto di partenza per successivi studi di potenziamento dell’attività farmacologica. Parallelamente, un altro aspetto fondamentale della ricerca farmacologica è rappresentato dall’identificazione dei targets di interazione per composti dalla nota bioattività, e questo risulta particolarmente interessante per i composti di origine naturale. Infatti, per tale classe di molecole sarebbe molto utile effettuare tests biologici su un elevato numero di recettori, ma ciò risulta spesso proibitivo a causa della scarsa quantità di composto disponibile. Partendo da tali presupposti, nella presente tesi è descritto approfonditamente un nuovo metodo computazionale definito Inverse Virtual Screening. I vari lavori basati su questo nuovo tipo di approccio sono stati effettuati considerando pannelli composti da diversi targets coinvolti nello sviluppo del cancro, portando all’identificazione della specifica attività antitumorale dei vari composti naturali investigati. Gli studi basati sull’Inverse Virtual Screening sono stati effettuati attraverso calcoli di Molecular Docking utilizzando diversi composti naturali, raggruppati in piccole librerie o studiati singolarmente. In primo luogo, è stato proposto un metodo matematico con l’obiettivo di escludere i falsi positivi e i falsi negativi applicando una normalizzazione delle affinità di legame predette (espresse in kcal/mol). Successivamente, tale approccio è stato applicato su una libreria di 10 composti di origine naturale, validando l’applicabilità di tale metodo attraverso tests biologici in vitro. Successivamente, un ulteriore studio è stato incentrato su un ciclopeptide definito namalide, la cui attività biologica su Carbossipeptidasi A era in totale accordo con i dati provenienti dallo studio di Inverse Virtual Screening condotto. Il Virtual Screening è stato inoltre studiato anche analizzando l’efficacia dei metodi per il calcolo accurato delle affinità di legame basati su simulazioni di Dinamica Molecolare. Tale studio è stato condotto su una libreria di 1588 composti (44 ligandi + 1544 decoys, estratti dal DUD database) sul target tripsina, utilizzando il metodo LIE (Linear Interaction Energy) attraverso un elevato numero di simulazioni di Dinamica Molecolare. Sono stati ottenuti quattro differenti scale di affinità predetta (attraverso quattro combinazioni di differenti scaling factors) e sono stati confrontati con i risultati derivanti dai calcoli di Molecular Docking, valutando e confrontando curve ROC e di enrichment. Attraverso il metodo LIE sono stati ottenuti risultati non incoraggianti, e quindi ulteriori analisi attraverso metodi MM-GBSA e MM-PBSA sono in corso di studio. Inoltre, screenings in silico sono stati effettuati anche per lo studio dettagliato di altri composti naturali la cui attività era nota a priori. Attraverso questa procedura, sono stati proposti diversi modelli di binding di una libreria di composti sul target PXR, e per tali composti è stata razionalizzata l’attività/inattività confrontando il loro binding mode con quello del Solomonsterol A, utilizzato come composto di riferimento su tale target. La presenza/assenza di attività biologica è stata è stata descritto a livello molecolare per un’altra classe di composti estratti dalla spugna Plakinastrella Mamillaris sul target PPAR-γ e sul diterpene oridonina sul target HSP70 1A attraverso esperimenti combinati di Molecular Docking e Molecular Dynamics. Sono stati proposti e descritti approfonditamente modelli di binding di tali composti, valutando come specifiche interazioni ligando-target macromolecolare (di natura idrofobica, elettrostatica o caratterizzata dalla presenza di specifici legami ad idrogeno) possano influenzare l’attività biologica. [a cura dell'autore]
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Rowlatt, Jack D. "Characterisation of putative glycan and drug binding proteins predicted using in silico screening methods." Thesis, Griffith University, 2020. http://hdl.handle.net/10072/397633.
Повний текст джерелаThesis (Masters)
Doctor of Philosophy (PhD)
School of Medical Science
Griffith Health
Full Text
Salentin, Sebastian. "In Silico Identification of Novel Cancer Drugs with 3D Interaction Profiling." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-226435.
Повний текст джерелаYoungs, Louise Claire. "Evaluation of in silico and in vitro screening methods for characterising endocrine disrupting chemical hazards." Thesis, Cranfield University, 2014. http://dspace.lib.cranfield.ac.uk/handle/1826/9717.
Повний текст джерелаElkaïm, Judith. "Drug design in silico : criblage virtuel de protéines à visée thérapeutique." Thesis, Bordeaux 1, 2011. http://www.theses.fr/2011BOR14444/document.
Повний текст джерелаThe process of drug discovery is long and tedious. Besides, it is relatively inefficient in terms of hit rate. The identification of candidates through experimental testing is expensive and requires extensive data on the mechanisms of the target protein in order to develop efficient assays. Virtual screening can considerably accelerate the process by quickly evaluating large databases of compounds and determining the most likely to bind to a target. Some success stories have emerged in the field over the last few years.The objectives of this work were first, to compare common tools and strategies for structure-based virtual screening, and second, to apply those tools to actual target proteins implied notably in carcinogenesis.In order to evaluate the docking and scoring programs available, the protein kinase GSK3 and a test set of known ligands were used as a model to perform methodological studies. In particular the influence of the flexibility of the protein was explored via relaxed structures of the receptor or the insertion of torsions on the side chains of residues located in the binding site. Studies concerning the automatic generation of 3D structures for the ligands and the use of consensus scoring also provided insights on the usability of these tools while performing a virtual screening.Virtual screening of the human protein Pontin, an ATPase implied in tumor cell growth for which no inhibitors were known, allowed the prioritization of compounds from commercial databases. These compounds were tested in an enzymatic assay via a collaboration, and led to the identification of four molecules capable of inhibiting the ATPase activity of Pontin. Additional screens of in-house oriented databases also provided at least one innovative inhibitor for this protein. On the contrary, a study of the human PLA2-X, a phospholipase that requires a Ca2+ atom to bind to its active site in order to catalyze the hydrolysis of its substrate, revealed the limits of our docking tools that could not handle the metal ion and the need for new tools
Книги з теми "SILICO SCREENING"
Uchida, Shizuka. Annotating new genes: From in silico screening to experimental validation. Oxford: Woodhead Publishing Limited, 2012.
Знайти повний текст джерела1st, Naga Raju Chinthakunta, Suresh Kumar Chitta 2nd, Anuradha C. M. 3rd, and Rajani Vallepu IV. In Silico Screening of Natural Compounds As Potential Inhibitors of GPR120 to Prevent Cancer. INSC International Publisher (IIP), 2021.
Знайти повний текст джерелаAtta-ur-Rahman and M. Iqbal Choudhary, eds. Frontiers in Cardiovascular Drug Discovery: Volume 4. BENTHAM SCIENCE PUBLISHERS, 2019. http://dx.doi.org/10.2174/97816810839951180401.
Повний текст джерелаLin, C. W., N. F. Chiu, and C. C. Chang. Modulation design of plasmonics for diagnostic and drug screening. Edited by A. V. Narlikar and Y. Y. Fu. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.013.18.
Повний текст джерелаNarlikar, A. V., and Y. Y. Fu, eds. Oxford Handbook of Nanoscience and Technology. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199533060.001.0001.
Повний текст джерелаЧастини книг з теми "SILICO SCREENING"
Luque, F. J., and X. Barril. "In Silico Screening." In Protein Surface Recognition, 211–35. Chichester, UK: John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470972137.ch8.
Повний текст джерелаEgbuna, Chukwuebuka, Santwana Palai, Israel Ehizuelen Ebhohimen, Andrew G. Mtewa, Jonathan C. Ifemeje, Genevieve D. Tupas, and Toskë L. Kryeziu. "Screening of Natural Antidiabetic Agents." In Phytochemistry: An in-silico and in-vitro Update, 203–35. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6920-9_11.
Повний текст джерелаK. C., Dhanya, Aditya Menon, and Laxmi Shanker Rai. "In-vitro Models in Anticancer Screening." In Phytochemistry: An in-silico and in-vitro Update, 251–65. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6920-9_13.
Повний текст джерелаLi, Shawn S. C., and Lei Li. "SH2 Ligand Prediction–Guidance for In-Silico Screening." In Methods in Molecular Biology, 77–81. New York, NY: Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6762-9_5.
Повний текст джерелаFischer, B., H. Merlitz, and W. Wenzel. "Increasing Diversity in In-silico Screening with Target Flexibility." In Lecture Notes in Computer Science, 186–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11560500_17.
Повний текст джерелаMerlitz, Holger, and Wolfgang Wenzel. "High Throughput in-silico Screening against Flexible Protein Receptors." In Computational Science and Its Applications – ICCSA 2004, 465–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24767-8_48.
Повний текст джерелаThrithamarassery Gangadharan, Nandu, Ananda Baskaran Venkatachalam, and Shiburaj Sugathan. "High-Throughput and In Silico Screening in Drug Discovery." In Bioresources and Bioprocess in Biotechnology, 247–73. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3573-9_11.
Повний текст джерелаMyrianthopoulos, Vassilios, George Lambrinidis, and Emmanuel Mikros. "In Silico Screening of Compound Libraries Using a Consensus of Orthogonal Methodologies." In Methods in Molecular Biology, 261–77. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8630-9_15.
Повний текст джерелаAkhoon, Bashir A., Krishna P. Singh, Madhumita Karmakar, Suchi Smita, Rakesh Pandey, and Shailendra K. Gupta. "Virtual screening and prediction of the molecular mechanism of bioactive compoundsin silico." In Biotechnology of Bioactive Compounds, 371–94. Chichester, UK: John Wiley & Sons, Ltd, 2015. http://dx.doi.org/10.1002/9781118733103.ch15.
Повний текст джерелаSrivastava, Supriya, and Puniti Mathur. "In Silico Modeling and Screening Studies of PfRAMA Protein: Implications in Malaria." In Recent Studies on Computational Intelligence, 91–101. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-8469-5_8.
Повний текст джерелаТези доповідей конференцій з теми "SILICO SCREENING"
Arrigo, Patrizio, Norbert Maggi, and Carmelina Ruggiero. "In silico screening of Rac1 ligand specificity." In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2008. http://dx.doi.org/10.1109/iembs.2008.4650110.
Повний текст джерелаOsman, Ahmed M., and Hanaa M. Alam EL-Din. "In-Silico Screening of Potential Anti-Glycoprotein of Nipah Virus." In 2021 Tenth International Conference on Intelligent Computing and Information Systems (ICICIS). IEEE, 2021. http://dx.doi.org/10.1109/icicis52592.2021.9694143.
Повний текст джерелаPal, Rajesh, Gauri Misra, and Puniti Mathur. "In silico screening of small molecule modulators of Zika virus proteins." In 2017 7th International Conference on Cloud Computing, Data Science & Engineering - Confluence (Confluence). IEEE, 2017. http://dx.doi.org/10.1109/confluence.2017.7943179.
Повний текст джерелаFukunishi, Yoshifumi. "In Silico Drug Screening Based on a Protein-Compound Affinity Matrix." In 2008 International Conference on Biocomputation, Bioinformatics, and Biomedical Technologies (BIOTECHNO). IEEE, 2008. http://dx.doi.org/10.1109/biotechno.2008.10.
Повний текст джерелаSoonwook Hwang, Sehoon Lee, Sangdo Lee, Jincheol Kim, Hanh Thi Thanh Nguyen, Doman Kim, Vincent Breton, and Jean Salzemann. "A grid-enabled problem solving environment for in-silico screening in drug discovery." In 2010 5th International Conference on Computer Sciences and Convergence Information Technology (ICCIT 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccit.2010.5711070.
Повний текст джерелаFrolenko, V. S., V. I. Uvarova, A. A. Nikitina, E. V. Khvatov, M. Dodina, E. Bayurova, L. I. Kozlovskaya, and D. I. Osolodkin. "SCREENING OF TICK-BORNE ENCEPHALITIS VIRUS METHYLTRANSFERASE INHIBITORS IN VITRO AND IN SILICO." In MedChem-Russia 2021. 5-я Российская конференция по медицинской химии с международным участием «МедХим-Россия 2021». Издательство Волгоградского государственного медицинского университета, 2021. http://dx.doi.org/10.19163/medchemrussia2021-2021-323.
Повний текст джерелаLida Zhu, Fengji Liang, Juan Liu, Simon Rayner, Yinghui Li, Shanguang Chen, and Jianghui Xiong. "Dynamic remodeling of context-specific miRNAs regulation networks facilitate in silico cancer drug screening." In 2011 IEEE International Conference on Systems Biology (ISB). IEEE, 2011. http://dx.doi.org/10.1109/isb.2011.6033168.
Повний текст джерелаde Jonge, Marc R., H. Maarten Vinkers, Joop H. van Lenthe, Frits Daeyaert, Ian J. Bush, Huub J. J. van Dam, Paul Sherwood, et al. "Ab Initio potential grid based docking: From High Performance Computing to In Silico Screening." In COMPLIFE 2007: The Third International Symposium on Computational Life Science. AIP, 2007. http://dx.doi.org/10.1063/1.2793399.
Повний текст джерелаAsaduzzaman, Md, Jahangir Alom, Mohammad Taufiq Alam, and Md Anwarul Karim. "In-silico Screening of Arjun Plant (Terminalia arjuna) Compounds Against Cardiovascular Disease Targeting GRK2 Protein." In 2021 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2). IEEE, 2021. http://dx.doi.org/10.1109/ic4me253898.2021.9768431.
Повний текст джерелаOmix Yu-Chian Chen. "Pharmacoinformatics approach to the discovery of novel selective COX-2 inhibitors by in silico virtual screening." In 2008 IEEE International Joint Conference on Neural Networks (IJCNN 2008 - Hong Kong). IEEE, 2008. http://dx.doi.org/10.1109/ijcnn.2008.4633958.
Повний текст джерелаЗвіти організацій з теми "SILICO SCREENING"
Rafaeli, Ada, Russell Jurenka, and Chris Sander. Molecular characterisation of PBAN-receptors: a basis for the development and screening of antagonists against Pheromone biosynthesis in moth pest species. United States Department of Agriculture, January 2008. http://dx.doi.org/10.32747/2008.7695862.bard.
Повний текст джерелаRafaeli, Ada, and Russell Jurenka. Molecular Characterization of PBAN G-protein Coupled Receptors in Moth Pest Species: Design of Antagonists. United States Department of Agriculture, December 2012. http://dx.doi.org/10.32747/2012.7593390.bard.
Повний текст джерела