Literatura académica sobre el tema "Prediction of binding affinity"
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Artículos de revistas sobre el tema "Prediction of binding affinity"
Wang, Debby D., Haoran Xie y Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity". Bioinformatics 37, n.º 17 (27 de febrero de 2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.
Texto completoKondabala, Rajesh, Vijay Kumar, Amjad Ali y Manjit Kaur. "A novel astrophysics-based framework for prediction of binding affinity of glucose binder". Modern Physics Letters B 34, n.º 31 (25 de julio de 2020): 2050346. http://dx.doi.org/10.1142/s0217984920503467.
Texto completoAntunes, Dinler A., Jayvee R. Abella, Didier Devaurs, Maurício M. Rigo y Lydia E. Kavraki. "Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes". Current Topics in Medicinal Chemistry 18, n.º 26 (24 de enero de 2019): 2239–55. http://dx.doi.org/10.2174/1568026619666181224101744.
Texto completoKwon, Yongbeom, Woong-Hee Shin, Junsu Ko y Juyong Lee. "AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks". International Journal of Molecular Sciences 21, n.º 22 (10 de noviembre de 2020): 8424. http://dx.doi.org/10.3390/ijms21228424.
Texto completoShar, Piar Ali, Weiyang Tao, Shuo Gao, Chao Huang, Bohui Li, Wenjuan Zhang, Mohamed Shahen, Chunli Zheng, Yaofei Bai y Yonghua Wang. "Pred-binding: large-scale protein–ligand binding affinity prediction". Journal of Enzyme Inhibition and Medicinal Chemistry 31, n.º 6 (18 de febrero de 2016): 1443–50. http://dx.doi.org/10.3109/14756366.2016.1144594.
Texto completoNguyen, Austin, Abhinav Nellore y Reid F. Thompson. "Discordant results among major histocompatibility complex binding affinity prediction tools". F1000Research 12 (7 de junio de 2023): 617. http://dx.doi.org/10.12688/f1000research.132538.1.
Texto completoLangham, James J., Ann E. Cleves, Russell Spitzer, Daniel Kirshner y Ajay N. Jain. "Physical Binding Pocket Induction for Affinity Prediction". Journal of Medicinal Chemistry 52, n.º 19 (8 de octubre de 2009): 6107–25. http://dx.doi.org/10.1021/jm901096y.
Texto completoÖztürk, Hakime, Arzucan Özgür y Elif Ozkirimli. "DeepDTA: deep drug–target binding affinity prediction". Bioinformatics 34, n.º 17 (1 de septiembre de 2018): i821—i829. http://dx.doi.org/10.1093/bioinformatics/bty593.
Texto completoWang, Xun, Dayan Liu, Jinfu Zhu, Alfonso Rodriguez-Paton y Tao Song. "CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction". Biomolecules 11, n.º 5 (27 de abril de 2021): 643. http://dx.doi.org/10.3390/biom11050643.
Texto completoPantsar, Tatu y Antti Poso. "Binding Affinity via Docking: Fact and Fiction". Molecules 23, n.º 8 (30 de julio de 2018): 1899. http://dx.doi.org/10.3390/molecules23081899.
Texto completoTesis sobre el tema "Prediction of binding affinity"
Jovanovic, Srdan. "Rapid, precise and reproducible binding affinity prediction : applications in drug discovery". Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10053853/.
Texto completoUslan, Volkan. "Support vector machine-based fuzzy systems for quantitative prediction of peptide binding affinity". Thesis, De Montfort University, 2015. http://hdl.handle.net/2086/11170.
Texto completoBodnarchuk, Michael. "Predicting the location and binding affinity of small molecules in protein binding sites". Thesis, University of Southampton, 2012. https://eprints.soton.ac.uk/348170/.
Texto completoErdas, Ozlem. "Modelling And Predicting Binding Affinity Of Pcp-like Compounds Using Machine Learning Methods". Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/3/12608792/index.pdf.
Texto completoGuedes, Isabella Alvim. "Development of empirical scoring funcions forn predicting proteinligand binding affinity". Laboratório Nacional de Computação Científica, 2016. https://tede.lncc.br/handle/tede/247.
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Molecular docking is a methodology that aims to predict the binding modes and affinity of a small molecule within the binding site of the receptor target of interest. It is an approach widely used by the pharmaceutical industry and the academic community for identification and optimization of lead compounds, contributing to the reduction of cost, time and failures in the development of new drugs. Current docking methods and the associated scoring functions exhibit good performances in identifying experimental binding modes. However, the detection of active compounds among a decoy set of ligands and the accurate prediction of binding affinity remain challenging tasks. The DockThor program developed in our group has obtained promising results in comparative studies with other well established and widely used protein-ligand docking programs for predicting experimental binding modes. Despite useful for pose prediction, the current scoring function implemented in DockThor is not suitable for predicting binding affinities of protein-ligand complexes, obtaining no correlation with measured affinity data. In this work, we develop several scoring functions with physically-based features for predicting binding affinities of protein-ligand complexes trained with diverse machine learning techniques. The final scoring functions consist of force-field based terms related to the intermolecular interactions (electrostatic and van der Waals potentials), an original term for the ligand entropy (number of frozen rotatable bonds), ligand and protein desolvation and the hydrophobic effect. Then, we developed general and target-classes scoring functions, the last to account for binding characteristics associated with a target class of interest, focusing on proteases, kinases and protein-protein interactions complexes (PPIs). The scoring functions were derived using linear regression (MLR) and seven more advanced machine learning techniques for nonlinear problems. The training and testing were carried out using high-quality datasets composed of experimental structures of diverse protein-ligand complexes with binding affinities data available (Kd or Ki). Additionally, we also derived general scoring functions trained with redocking results from the DockThor program. The scoring functions trained with docking results obtained promising performances when evaluated in both experimental and docking structures, indicating that they are reliable to be used in real virtual screening experiments. The scoring functions developed in this work have demonstrated to be competitive with the best-evaluated linear and nonlinear scoring functions in benchmarking studies described in the literature. The scoring functions derived for specific classes of targets also exhibited promising performances, achieving great improvements when using nonlinear approaches compared to the linear models. Moreover, the consensus scoring strategy investigated in this work exhibited impressive results, ranking among the top-three models with the best predictive performances on all cases. The development of the scoring functions implemented in this thesis is a crucial step to make the DockThor an even more competitive program, enabling the development of the high-throughput virtual screening program and portal DockThor-VS.
Atracamento molecular é uma metodologia que tem por objetivo prever a conformação e a afinidade de uma pequena molécula no sítio de ligação do receptor alvo de interesse. É uma abordagem amplamente utilizada pela indústria farmacêutica e pela comunidade acadêmica para identificação e otimização de compostos líderes, contribuindo para a redução de custo, tempo e falhas no desenvolvimento de novos fármacos. As metodologias atuais de atracamento molecular e as funções de avaliação associadas possuem bom desempenho em identificar modos de ligação. Entretanto, a detecção de compostos ativos dentre inativos e a predição acurada da afinidade de ligação ainda são grandes desafios. O programa DockThor, desenvolvido pelo nosso grupo de pesquisa, tem obtido resultados promissores em estudos comparativos com outros programas de atracamento molecular bem estabelecidos e amplamente utilizados pela comunidade científica para a predição de modos de ligação. Apesar de ser bastante útil para predição de poses, a função de avaliação atualmente implementada no DockThor não é adequada para prever afinidade de complexos proteína-ligante, não obtendo correlação com dados experimentais. Neste trabalho, nós desenvolvemos diversas funções de avaliação com características baseadas na física para prever afinidade de ligação de complexos proteína-ligante, treinadas com diversas técnicas de aprendizagem de máquina. As funções de avaliação finais consistem de termos baseados em campo de força relacionados com as interações intermoleculares (potenciais eletrostático e de van der Waals), um termo original para a entropia do ligante (número de ligações rotacionáveis congeladas), dessolvatação do ligante e da proteína e o efeito hidrofóbico. Desenvolvemos então funções de avaliação gerais e específicas para classes de alvos, esta para considerar características específicas associadas com a classe de alvo de interesse, focando em proteases, cinases e complexos de interações proteína-proteína (PPIs). As funções de avaliação foram derivadas utilizando regressão linear (MLR) e sete outras técnicas mais avançadas de aprendizagem de máquina para problemas não lineares. O processo de treinamento e teste foi realizado utilizando conjuntos de dados de alta qualidade compostos de estruturas experimentais de diversos complexos proteína-ligante com dados de afinidade de ligação disponíveis (Kd ou Ki). Adicionalmente, também derivamos funções de avaliação gerais treinadas com resultados do atracamento molecular com o programa DockThor. As funções treinadas com resultados de atracamento obtiveram desempenho promissor quando avaliadas tanto em estruturas experimentais quanto provenientes de atracamento molecular, indicando que elas são confiáveis para serem usadas em experimentos reais de triagem virtual. As funções desenvolvidas neste trabalho demonstraram ser competitivas com as melhores funções de avaliação lineares e não lineares em estudos comparativos descritas na literatura. As funções específicas para classes de alvos também exibiram desempenhos promissores, alcançando significativa melhoria quando utilizando abordagens não lineares comparadas com os modelos lineares. Além disso, a estratégia de avaliação consenso investigada neste trabalho exibiu resultados impressionantes, ficando entre os três melhores modelos com melhores desempenhos preditivos em todos os casos. O desenvolvimento das funções de avaliação implementadas nesta tese é um passo crucial para tornar o programa DockThor ainda mais competitivo, possibilitando o desenvolvimento do programa e do portal de triagem virtual em larga escala DockThor-VS.
Matereke, Lavious Tapiwa. "Analysis of predictive power of binding affinity of PBM-derived sequences". Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018666.
Texto completoYoldas, Mine. "Predicting The Effect Of Hydrophobicity Surface On Binding Affinity Of Pcp-like Compounds Using Machine Learning Methods". Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613215/index.pdf.
Texto completoShoemake, Claire. "The use of static and dynamic models for the prediction of ligand binding affinity using oestrogen and androgen nuclear receptors as case studies". Thesis, University of Nottingham, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.478985.
Texto completoAtkovska, Kalina, Sergey A. Samsonov, Maciej Paszkowski-Rogacz y M. Teresa Pisabarro. "Multipose Binding in Molecular Docking". Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2014. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-147177.
Texto completoNordesjö, Olle. "Searching for novel protein-protein specificities using a combined approach of sequence co-evolution and local structural equilibration". Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-275040.
Texto completoLibros sobre el tema "Prediction of binding affinity"
Krishna, Mallia A. y Smith Paul K, eds. Immobilized affinity ligand techniques. San Diego: Academic Press, 1992.
Buscar texto completoNaples, Mark. Determinants of high affinity ligand binding to the group III metabotropic glutamate receptors. Ottawa: National Library of Canada, 2001.
Buscar texto completo1958-, McMahon Robert Joseph, ed. Avidin-biotin interactions: Methods and applications. Totowa, NJ: Humana, 2008.
Buscar texto completoMarles, Jennifer Anne. Significance of the ligand-binding affinity of the Sho1 SH3 domain for in vivo function. Ottawa: National Library of Canada, 2003.
Buscar texto completoPuvvada, Madhu. Investigation into the relationship between DNA-binding affinity, sequence-specificity and biological activity in the pyrrolo[2,1-c][1,4]benzodiazepine group of antitumour antibiotics. Portsmouth: University of Portsmouth, Division of Medicinal Chemistry, 1995.
Buscar texto completoMarelius, John. Computational Prediction of Receptor-Ligand Binding Affinity in Drug Discovery. Uppsala Universitet, 2000.
Buscar texto completoMallia, A. Krishna. Immobilized Affinity Ligand Techniques. Elsevier Science & Technology Books, 2012.
Buscar texto completoAffinity and Efficacy. World Scientific Publishing Company, 2011.
Buscar texto completoVerotoxin-globotriosylceramide binding: Receptor affinity purification and the effect of membrane environment on toxin binding. Ottawa: National Library of Canada, 1993.
Buscar texto completoBanks, Robert C. Oligodeoxynucleotide affinity column for the isolation of sequence specific DNA binding proteins. 1989.
Buscar texto completoCapítulos de libros sobre el tema "Prediction of binding affinity"
Takaba, Kenichiro. "Application of FMO for Protein–ligand Binding Affinity Prediction". En Recent Advances of the Fragment Molecular Orbital Method, 281–94. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9235-5_13.
Texto completoLu, Yaoyao, Junkai Liu, Tengsheng Jiang, Shixuan Guan y Hongjie Wu. "Protein-Ligand Binding Affinity Prediction Based on Deep Learning". En Intelligent Computing Theories and Application, 310–16. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13829-4_26.
Texto completoAsha, P. R. y M. S. Vijaya. "Binding Affinity Prediction Models for Spinocerebellar Ataxia Using Supervised Learning". En Communications in Computer and Information Science, 145–52. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1423-0_17.
Texto completoLiu, Wen, Ji Wan, Xiangshan Meng, Darren R. Flower y Tongbin Li. "In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC". En Methods in Molecular Biology, 283–91. Totowa, NJ: Humana Press, 2007. http://dx.doi.org/10.1007/978-1-60327-118-9_20.
Texto completoLi, Xueling, Min Zhu, Xiaolai Li, Hong-Qiang Wang y Shulin Wang. "Protein-Protein Binding Affinity Prediction Based on an SVR Ensemble". En Lecture Notes in Computer Science, 145–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31588-6_19.
Texto completoNikam, Rahul, K. Yugandhar y M. Michael Gromiha. "Discrimination and Prediction of Protein-Protein Binding Affinity Using Deep Learning Approach". En Intelligent Computing Theories and Application, 809–15. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_89.
Texto completoXia, Minghao, Jing Hu, Xiaolong Zhang y Xiaoli Lin. "Drug-Target Binding Affinity Prediction Based on Graph Neural Networks and Word2vec". En Intelligent Computing Theories and Application, 496–506. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13829-4_43.
Texto completoYang, Yuedong, Huiying Zhao, Jihua Wang y Yaoqi Zhou. "SPOT-Seq-RNA: Predicting Protein–RNA Complex Structure and RNA-Binding Function by Fold Recognition and Binding Affinity Prediction". En Methods in Molecular Biology, 119–30. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0366-5_9.
Texto completoAzzopardi, Joseph y Jean Paul Ebejer. "LigityScore: A CNN-Based Method for Binding Affinity Predictions". En Biomedical Engineering Systems and Technologies, 18–44. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20664-1_2.
Texto completoLi, Hongjian, Kwong-Sak Leung, Man-Hon Wong y Pedro J. Ballester. "The Impact of Docking Pose Generation Error on the Prediction of Binding Affinity". En Computational Intelligence Methods for Bioinformatics and Biostatistics, 231–41. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24462-4_20.
Texto completoActas de conferencias sobre el tema "Prediction of binding affinity"
Atwereboannah, Abena Achiaa, Wei-Ping Wu, Lei Ding, Sophyani B. Yussif y Edwin Kwadwo Tenagyei. "Protein-Ligand Binding Affinity Prediction Using Deep Learning". En 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2021. http://dx.doi.org/10.1109/iccwamtip53232.2021.9674118.
Texto completoLi, Yanjun, Mohammad A. Rezaei, Chenglong Li y Xiaolin Li. "DeepAtom: A Framework for Protein-Ligand Binding Affinity Prediction". En 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8982964.
Texto completoLi, Mei, Sihan Xu, Xiangrui Cai, Zhong Zhang y Hua Ji. "Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction". En 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2022. http://dx.doi.org/10.1109/bibm55620.2022.9995372.
Texto completoD'Souza, Sofia, K. V. Prema y S. Balaji. "Hierarchical Modeling of Binding Affinity Prediction Using Machine LearningTechniques". En 2021 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER). IEEE, 2021. http://dx.doi.org/10.1109/discover52564.2021.9663690.
Texto completoCong, Chunyu, Pingping Sun y Zhiqiang Ma. "Predicting binding affinity using differential evolution". En 2012 5th International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2012. http://dx.doi.org/10.1109/bmei.2012.6513124.
Texto completoZhao, Qichang, Fen Xiao, Mengyun Yang, Yaohang Li y Jianxin Wang. "AttentionDTA: prediction of drug–target binding affinity using attention model". En 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2019. http://dx.doi.org/10.1109/bibm47256.2019.8983125.
Texto completoChyan, Jeffrey, Mark Moll y Lydia E. Kavraki. "Improving the Prediction of Kinase Binding Affinity Using Homology Models". En BCB'13: ACM-BCB2013. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2506583.2506704.
Texto completoYaseen, Adiba, Wajid Arshad Abbasi y Fayyaz ul Amir Afsar Minhas. "Protein binding affinity prediction using support vector regression and interfecial features". En 2018 15th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE, 2018. http://dx.doi.org/10.1109/ibcast.2018.8312222.
Texto completoZhao, Lingling, Peijin Xie, Lingfeng Hao, Tiantian Li y Chunyu Wang. "Gene Ontology aided Compound Protein Binding Affinity Prediction Using BERT Encoding". En 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2020. http://dx.doi.org/10.1109/bibm49941.2020.9312985.
Texto completoFeng, Xianbing, Jingwei Qu, Tianle Wang, Bei Wang, Xiaoqing Lyu y Zhi Tang. "Attention-enhanced Graph Cross-convolution for Protein-Ligand Binding Affinity Prediction". En 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2021. http://dx.doi.org/10.1109/bibm52615.2021.9669341.
Texto completoInformes sobre el tema "Prediction of binding affinity"
Warren, H. S. Purification of LPS Binding Factors in Tolerant Serum by Affinity Chromatography. Fort Belvoir, VA: Defense Technical Information Center, marzo de 1991. http://dx.doi.org/10.21236/ada233638.
Texto completoReiff, Emily A. y Gunda I. Georg. Construction of Affinity Probes to Study the Epothilone Binding Site on Tubulin. Fort Belvoir, VA: Defense Technical Information Center, mayo de 2003. http://dx.doi.org/10.21236/ada416670.
Texto completoStratis-Cullum, Dimitra N., Sun McMasters y Paul M. Pellegrino. Affinity Probe Capillary Electrophoresis Evaluation of Aptamer Binding to Campylobacter jejuni Bacteria. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 2009. http://dx.doi.org/10.21236/ada512469.
Texto completoFresco, Jacques R. Development of affinity technology for isolating individual human chromosomes by third strand binding. Office of Scientific and Technical Information (OSTI), junio de 2003. http://dx.doi.org/10.2172/820632.
Texto completoPattabiraman, Nagarajan, Carolyn Chambers, Ayesha Adil y Gregory E. Garcia. Identification of Small Molecules against Botulinum Neurotoxin B Binding to Neuronal Cells at Ganglioside GT1b Binding Site with Low to Moderate Affinity. Fort Belvoir, VA: Defense Technical Information Center, octubre de 2014. http://dx.doi.org/10.21236/ada612876.
Texto completoChefetz, Benny, Baoshan Xing y Yona Chen. Interactions of engineered nanoparticles with dissolved organic matter (DOM) and organic contaminants in water. United States Department of Agriculture, enero de 2013. http://dx.doi.org/10.32747/2013.7699863.bard.
Texto completoBennion, B., K. Kulp, M. Cosman y F. Lightstone. Computational Characterization and Prediction of Estrogen Receptor Coactivator Binding Site Inhibitors. Office of Scientific and Technical Information (OSTI), agosto de 2005. http://dx.doi.org/10.2172/900142.
Texto completoBennion, Brian J., Kris Kulp, Monique Cosman y Felice Lightstone. Computational Characterization and Prediction of Estrogen Receptor Coactivator Binding Site Inhibitors. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2005. http://dx.doi.org/10.21236/ada446323.
Texto completoMills, Gordon B. Detection of Serum Lysophosphatidic Acids Using Affinity Binding and Surface Enhanced Laser Desorption/Ionization (SELDI) Time of Flight Mass Spectrometry. Fort Belvoir, VA: Defense Technical Information Center, abril de 2005. http://dx.doi.org/10.21236/ada437186.
Texto completoMills, Gordon B. Detection of Serum Lysophosphatidic Acids Using Affinity Binding and Surface Enhanced Laser Deorption/Ionization (SELDI) Time of Flight Mass Spectrometry. Fort Belvoir, VA: Defense Technical Information Center, abril de 2006. http://dx.doi.org/10.21236/ada455094.
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