Relevant bibliographies by topics / Ligand based virtual screening

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Ligand based virtual screening'

Author: Grafiati
Published: 4 June 2021
Last updated: 29 January 2023

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Journal articles on the topic "Ligand based virtual screening"

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Kato, Koya, and George Chikenji. "1P266 Development of Ligand Based Virtual Screening considering protein-ligand interaction(22A. Bioinformatics: Structural genomics,Poster)." Seibutsu Butsuri 53, supplement1-2 (2013): S150. http://dx.doi.org/10.2142/biophys.53.s150_1.

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Douguet, Dominique. "Ligand-Based Approaches in Virtual Screening." Current Computer Aided-Drug Design 4, no. 3 (2008): 180–90. http://dx.doi.org/10.2174/157340908785747456.

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Stahura, Florence, and Jürgen Bajorath. "New Methodologies for Ligand-Based Virtual Screening." Current Pharmaceutical Design 11, no. 9 (2005): 1189–202. http://dx.doi.org/10.2174/1381612053507549.

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Ahmed, Ali, Naomie Salim, and Ammar Abdo. "Fragment Reweighting in Ligand-Based Virtual Screening." Advanced Science Letters 19, no. 9 (2013): 2782–86. http://dx.doi.org/10.1166/asl.2013.5012.

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HIRAYAMA, Noriaki. "Virtual Screening Based on Protein-Ligand Interactions." YAKUGAKU ZASSHI 127, no. 1 (2007): 101–2. http://dx.doi.org/10.1248/yakushi.127.101.

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Jain, Ajay N. "Ligand-Based Structural Hypotheses for Virtual Screening." Journal of Medicinal Chemistry 47, no. 4 (2004): 947–61. http://dx.doi.org/10.1021/jm030520f.

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Abdo, Ammar, Beining Chen, Christoph Mueller, Naomie Salim, and Peter Willett. "Ligand-Based Virtual Screening Using Bayesian Networks." Journal of Chemical Information and Modeling 50, no. 6 (2010): 1012–20. http://dx.doi.org/10.1021/ci100090p.

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Dai, Weixing, and Dianjing Guo. "A Ligand-Based Virtual Screening Method Using Direct Quantification of Generalization Ability." Molecules 24, no. 13 (2019): 2414. http://dx.doi.org/10.3390/molecules24132414.

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Machine learning plays an important role in ligand-based virtual screening. However, conventional machine learning approaches tend to be inefficient when dealing with such problems where the data are imbalanced and features describing the chemical characteristic of ligands are high-dimensional. We here describe a machine learning algorithm LBS (local beta screening) for ligand-based virtual screening. The unique characteristic of LBS is that it quantifies the generalization ability of screening directly by a refined loss function, and thus can assess the risk of over-fitting accurately and eff
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Kato, Koya, and George Chikenji. "2P272 A Ligand Based Virtual Screening method that takes into account of protein-ligand interactions(22A. Bioinformatics:Structural genomics,Poster)." Seibutsu Butsuri 54, supplement1-2 (2014): S240. http://dx.doi.org/10.2142/biophys.54.s240_2.

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Rayevsky, O. V., O. M. Demchyk, P. A. Karpov та ін. "Structure-based virtual screening for new lead compounds targeted Plasmodium α-tubulin". Faktori eksperimental'noi evolucii organizmiv 28 (31 серпня 2021): 135–39. http://dx.doi.org/10.7124/feeo.v28.1389.

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Aim. Search for new dinitroaniline and phosphorothioamide compounds, capable of selective binding with Plasmodium α-tubulin, affecting its mitotic apparatus. Methods. Structural biology methods of computational prediction of protein-ligand interaction: molecular docking, molecular dynamics and pharmacophore analysis. Selection of compounds based on pharmacophore characteristics and virtual screening results. Results. The protocol and required structural conditions for target (α-tubulin of P. falciparum) preparation and correct modeling of the ligand-protein interaction (docking and virtual scr
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Dissertations / Theses on the topic "Ligand based virtual screening"

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Shave, Steven R. "Development of high performance structure and ligand based virtual screening techniques." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4333.

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Virtual Sreening (VS) is an in silico technique for drug discovery. An overview of VS methods is given and is seen to be approachable from two sides: structure based and ligand based. Structure based virtual screening uses explicit knowledge of the target receptor to suggest candidate receptor-ligand complexes. Ligand based virtual screening can infer required characteristics of binders from known ligands. A consideration for all virtual screening techniques is the amount of computing time required to arrive at a solution. For this reason, techniques of high performance computing have been app
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Mazalan, Lucyantie. "Evaluation of similarity measures for ligand-based virtual screening." Thesis, University of Sheffield, 2017. http://etheses.whiterose.ac.uk/21422/.

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Alaasam, Mohammed. "Identification of novel monoamine oxidase B inhibitors from ligand based virtual screening." Kent State University / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=kent1405439915.

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Heikamp, Kathrin [Verfasser]. "Application and Development of Computational Methods for Ligand-Based Virtual Screening / Kathrin Heikamp." Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1052061036/34.

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Gregori, Puigjané Elisabet. "A new Ligand-Based approach to virtual screening, and prolifing or large chemical libraries." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7166.

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La representació de les molècules per mitjà de descriptors moleculars és la base de moltes de les eines computacionals pel disseny de fàrmacs. Aquests mètodes computacionals es basen en l'abstracció de l'estructura química per resumir aquelles característiques rellevants sent al mateix temps eficients en la comparació de grans llibreries de molècules. Una característica molt important d'aquests descriptors és la seva habilitat de capturar la informació rellevant per la interacció amb qualsevol proteïna independentment de l'esquelet del compost. Això permet detectar com a similars qualsevol pa
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Tai, Hio Kuan. "Protein-ligand docking and virtual screening based on chaos-embedded particle swarm optimization algorithm." Thesis, University of Macau, 2018. http://umaclib3.umac.mo/record=b3948431.

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Behren, Mathias Michael von Verfasser], and Matthias [Akademischer Betreuer] [Rarey. "Ligand-based Virtual Screening Utilizing Partial Shape Constraints / Mathias Michael von Behren ; Betreuer: Matthias Rarey." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2017. http://nbn-resolving.de/urn:nbn:de:gbv:18-86060.

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Behren, Mathias Michael von [Verfasser], and Matthias [Akademischer Betreuer] Rarey. "Ligand-based Virtual Screening Utilizing Partial Shape Constraints / Mathias Michael von Behren ; Betreuer: Matthias Rarey." Hamburg : Staats- und Universitätsbibliothek Hamburg, 2017. http://d-nb.info/113732371X/34.

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Santos, Alan Diego dos. "Ranking ligands in structure-based virtual screening using siamese neural networks." Pontif?cia Universidade Cat?lica do Rio Grande do Sul, 2017. http://tede2.pucrs.br/tede2/handle/tede/7763.

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Nawsheen, Sabia. "Evaluation of Fragment-Based Virtual Screening by Applying Docking on Fragments obtained from Optimized Ligands." Thesis, Uppsala universitet, Institutionen för läkemedelskemi, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-446388.

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Fragment-based virtual screening is an in-silico method that potentially identifies new startingpoints for drug molecules and provides an inexpensive and fast exploration of the relevantchemical space compared to its experimental counterpart. It focuses on docking small potentialbinding fragments to a binding pocket and is used to design improved binders by growing thefragments or joining fragments using suitable linkers. In this project, a fragment-based virtualscreening was evaluated by docking 21 fragments that are obtained from 4 different drugs. Here,the fragments were evaluated using SP
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Books on the topic "Ligand based virtual screening"

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Klebe, Gerhard. Virtual Screening: An Alternative or Complement to High Throughput Screening? Springer, 2010.

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1965-, Alvarez Juan, and Shoichet Brian 1963-, eds. Virtual screening in drug discovery. Taylor & Francis, 2005.

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(Editor), Juan Alvarez, and Brian Shoichet (Editor), eds. Virtual Screening in Drug Discovery. CRC, 2005.

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Gerhard, Klebe, ed. Virtual screening: An alternative or complement to high throughput screening : proceedings of the Workshop 'New Approaches in Drug Design and Discovery', special topic 'Virtual Screening', SchloB Rauischholzhausen, Germany, March 15-18, 1999. Kluwer Academic Publishers, 2000.

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Book chapters on the topic "Ligand based virtual screening"

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Koeppen, Herbert, Jan Kriegl, Uta Lessel, Christofer S. Tautermann, and Bernd Wellenzohn. "Ligand-Based Virtual Screening." In Methods and Principles in Medicinal Chemistry. Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527633326.ch3.

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Pitt, William R., Mark D. Calmiano, Boris Kroeplien, Richard D. Taylor, James P. Turner, and Michael A. King. "Structure-Based Virtual Screening for Novel Ligands." In Protein-Ligand Interactions. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-398-5_19.

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Abdo, Ammar, and Naomie Salim. "Ligand-Based Virtual Screening Using Bayesian Inference Network." In Library Design, Search Methods, and Applications of Fragment-Based Drug Design. American Chemical Society, 2011. http://dx.doi.org/10.1021/bk-2011-1076.ch004.

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Bhunia, Shome S., Mridula Saxena, and Anil K. Saxena. "Ligand- and Structure-Based Virtual Screening in Drug Discovery." In Biophysical and Computational Tools in Drug Discovery. Springer International Publishing, 2021. http://dx.doi.org/10.1007/7355_2021_130.

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Howe, Trevor, Daniele Bemporad, and Gary Tresadern. "Scenarios and Case Studies: Examples for Ligand-Based Virtual Screening." In Methods and Principles in Medicinal Chemistry. Wiley-VCH Verlag GmbH & Co. KGaA, 2011. http://dx.doi.org/10.1002/9783527633326.ch13.

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Matsuyama, Yusuke, and Takashi Ishida. "Stacking Multiple Molecular Fingerprints for Improving Ligand-Based Virtual Screening." In Intelligent Computing Theories and Application. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_35.

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Shin, Woong-Hee, and Daisuke Kihara. "Virtual Ligand Screening Using PL-PatchSurfer2, a Molecular Surface-Based Protein–Ligand Docking Method." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7756-7_7.

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Al-Dabbagh, Mohammed Mumtaz, Naomie Salim, and Faisal Saeed. "Methods to Improve Ranking Chemical Structures in Ligand-Based Virtual Screening." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-33582-3_25.

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Nasser, Maged, Naomie Salim, Hentabli Hamza, and Faisal Saeed. "Deep Belief Network for Molecular Feature Selection in Ligand-Based Virtual Screening." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-99007-1_1.

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Puertas-Martín, Savíns, Juana L. Redondo, Antonio J. Banegas-Luna, et al. "Virtual Screening Based on Electrostatic Similarity and Flexible Ligands." In Computational Science and Its Applications – ICCSA 2022 Workshops. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10562-3_10.

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Conference papers on the topic "Ligand based virtual screening"

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Skoda, Petr, and David Hoksza. "Benchmarking platform for ligand-based virtual screening." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822693.

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Skoda, Petr, David Hoksza, and Jan Jelinek. "Platform for ligand-based virtual screening integration." In 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2017. http://dx.doi.org/10.1109/bibm.2017.8218015.

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Babaria, Khushboo, Sanya Ambegaokar, Shubhankar Das, and Hemant Palivela. "Algorithms for ligand based virtual screening in drug discovery." In 2015 International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT). IEEE, 2015. http://dx.doi.org/10.1109/icatcct.2015.7457004.

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Palivela, Hemant, Divesh Kubal, and C. R. Nirmala. "Multiple kernel learning techniques for ligand based virtual screening." In 2017 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 2017. http://dx.doi.org/10.1109/iccci.2017.8117724.

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Ullrich, Katrin, Michael Kamp, Thomas Gartner, Martin Vogt, and Stefan Wrobel. "Ligand-Based Virtual Screening with Co-regularised Support Vector Regression." In 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). IEEE, 2016. http://dx.doi.org/10.1109/icdmw.2016.0044.

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Bahi, Meriem, and Mohamed Batouche. "Deep Learning for Ligand-Based Virtual Screening in Drug Discovery." In 2018 3rd International Conference on Pattern Analysis and Intelligent Systems (PAIS). IEEE, 2018. http://dx.doi.org/10.1109/pais.2018.8598488.

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Cavasotto, Claudio N. "Ligand Docking and Virtual Screening in Structure-based Drug Discovery." In FROM PHYSICS TO BIOLOGY: The Interface between Experiment and Computation - BIFI 2006 II International Congress. AIP, 2006. http://dx.doi.org/10.1063/1.2345621.

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"Deep Belief Networks for Ligand-Based Virtual Screening of Drug Design." In 2016 the 6th International Workshop on Computer Science and Engineering. WCSE, 2016. http://dx.doi.org/10.18178/wcse.2016.06.115.

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Park, Jung Woo, and Sung-Wha Hong. "Ligand- and Structure-based Virtual Screening Studies for the Discovery of Selective Inhibitors." In 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983013.

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Ahmed, Ali, Ammar Abdo, and Naomie Salim. "An enhancement of Bayesian inference network for ligand-based virtual screening using minifingerprints." In Fourth International Conference on Machine Vision (ICMV 11), edited by Zhu Zeng and Yuting Li. SPIE, 2011. http://dx.doi.org/10.1117/12.920338.

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Reports on the topic "Ligand based virtual screening"

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Altstein, Miriam, and Ronald J. Nachman. Rational Design of Insect Control Agent Prototypes Based on Pyrokinin/PBAN Neuropeptide Antagonists. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7593398.bard.

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The general objective of this study was to develop rationally designed mimetic antagonists (and agonists) of the PK/PBAN Np class with enhanced bio-stability and bioavailability as prototypes for effective and environmentally friendly pest insect management agents. The PK/PBAN family is a multifunctional group of Nps that mediates key functions in insects (sex pheromone biosynthesis, cuticular melanization, myotropic activity, diapause and pupal development) and is, therefore, of high scientific and applied interest. The objectives of the current study were: (i) to identify an antagonist bioph
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Rafaeli, Ada, and Russell Jurenka. Molecular Characterization of PBAN G-protein Coupled Receptors in Moth Pest Species: Design of Antagonists. United States Department of Agriculture, 2012. http://dx.doi.org/10.32747/2012.7593390.bard.

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The proposed research was directed at determining the activation/binding domains and gene regulation of the PBAN-R’s thereby providing information for the design and screening of potential PBAN-R-blockers and to indicate possible ways of preventing the process from proceeding to its completion. Our specific aims included: (1) The identification of the PBAN-R binding domain by a combination of: (a) in silico modeling studies for identifying specific amino-acid side chains that are likely to be involved in binding PBAN with the receptor and; (b) bioassays to verify the modeling studies using mut
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Eyal, Yoram, and Sheila McCormick. Molecular Mechanisms of Pollen-Pistil Interactions in Interspecific Crossing Barriers in the Tomato Family. United States Department of Agriculture, 2000. http://dx.doi.org/10.32747/2000.7573076.bard.

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During the evolutionary process of speciation in plants, naturally occurring barriers to reproduction have developed that affect the transfer of genes within and between related species. These barriers can occur at several different levels beginning with pollination-barriers and ending with hybrid-breakdown. The interaction between pollen and pistils presents one of the major barriers to intra- and inter-specific crosses and is the focus of this research project. Our long-term goal in this research proposal was defined to resolve questions on recognition and communication during pollen-pistil
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Altstein, Miriam, and Ronald Nachman. Rationally designed insect neuropeptide agonists and antagonists: application for the characterization of the pyrokinin/Pban mechanisms of action in insects. United States Department of Agriculture, 2006. http://dx.doi.org/10.32747/2006.7587235.bard.

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The general objective of this BARD project focused on rationally designed insect neuropeptide (NP) agonists and antagonists, their application for the characterization of the mechanisms of action of the pyrokinin/PBAN (PK-PBAN) family and the development of biostable, bioavailable versions that can provide the basis for development of novel, environmentally-friendly pest insect control agents. The specific objectives of the study, as originally proposed, were to: (i) Test stimulatory potencies of rationally designed backbone cyclic (BBC) peptides on pheromonotropic, melanotropic, myotropic and
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