Artículos de revistas sobre el tema "Prediction of binding affinity"
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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 completoKappel, Kalli, Inga Jarmoskaite, Pavanapuresan P. Vaidyanathan, William J. Greenleaf, Daniel Herschlag y Rhiju Das. "Blind tests of RNA–protein binding affinity prediction". Proceedings of the National Academy of Sciences 116, n.º 17 (8 de abril de 2019): 8336–41. http://dx.doi.org/10.1073/pnas.1819047116.
Texto completoKim, Ryangguk y Jeffrey Skolnick. "Assessment of programs for ligand binding affinity prediction". Journal of Computational Chemistry 29, n.º 8 (2008): 1316–31. http://dx.doi.org/10.1002/jcc.20893.
Texto completoMarshall, K. W., K. J. Wilson, J. Liang, A. Woods, D. Zaller y J. B. Rothbard. "Prediction of peptide affinity to HLA DRB1*0401." Journal of Immunology 154, n.º 11 (1 de junio de 1995): 5927–33. http://dx.doi.org/10.4049/jimmunol.154.11.5927.
Texto completoGim, Mogan, Junseok Choe, Seungheun Baek, Jueon Park, Chaeeun Lee, Minjae Ju, Sumin Lee y Jaewoo Kang. "ArkDTA: attention regularization guided by non-covalent interactions for explainable drug–target binding affinity prediction". Bioinformatics 39, Supplement_1 (1 de junio de 2023): i448—i457. http://dx.doi.org/10.1093/bioinformatics/btad207.
Texto completoWalpoth, Belinda Nazan y Burak Erman. "Regulation of ryanodine receptor RyR2 by protein-protein interactions: prediction of a PKA binding site on the N-terminal domain of RyR2 and its relation to disease causing mutations". F1000Research 4 (28 de enero de 2015): 29. http://dx.doi.org/10.12688/f1000research.5858.1.
Texto completoHenrich, Stefan, Isabella Feierberg, Ting Wang, Niklas Blomberg y Rebecca C. Wade. "Comparative binding energy analysis for binding affinity and target selectivity prediction". Proteins: Structure, Function, and Bioinformatics 78, n.º 1 (17 de agosto de 2009): 135–53. http://dx.doi.org/10.1002/prot.22579.
Texto completoLimbu, Sarita y Sivanesan Dakshanamurthy. "A New Hybrid Neural Network Deep Learning Method for Protein–Ligand Binding Affinity Prediction and De Novo Drug Design". International Journal of Molecular Sciences 23, n.º 22 (11 de noviembre de 2022): 13912. http://dx.doi.org/10.3390/ijms232213912.
Texto completoOUYANG, XUCHANG, STEPHANUS DANIEL HANDOKO y CHEE KEONG KWOH. "CSCORE: A SIMPLE YET EFFECTIVE SCORING FUNCTION FOR PROTEIN–LIGAND BINDING AFFINITY PREDICTION USING MODIFIED CMAC LEARNING ARCHITECTURE". Journal of Bioinformatics and Computational Biology 09, supp01 (diciembre de 2011): 1–14. http://dx.doi.org/10.1142/s021972001100577x.
Texto completoPandey, Mohit, Mariia Radaeva, Hazem Mslati, Olivia Garland, Michael Fernandez, Martin Ester y Artem Cherkasov. "Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks". Molecules 27, n.º 16 (11 de agosto de 2022): 5114. http://dx.doi.org/10.3390/molecules27165114.
Texto completoKalemati, Mahmood, Mojtaba Zamani Emani y Somayyeh Koohi. "BiComp-DTA: Drug-target binding affinity prediction through complementary biological-related and compression-based featurization approach". PLOS Computational Biology 19, n.º 3 (31 de marzo de 2023): e1011036. http://dx.doi.org/10.1371/journal.pcbi.1011036.
Texto completoUsha, Singaravelu y Samuel Selvaraj. "Prediction of kinase-inhibitor binding affinity using energetic parameters". Bioinformation 12, n.º 3 (15 de junio de 2016): 172–81. http://dx.doi.org/10.6026/97320630012172.
Texto completoDas, Sourav, Michael P. Krein y Curt M. Breneman. "Binding Affinity Prediction with Property-Encoded Shape Distribution Signatures". Journal of Chemical Information and Modeling 50, n.º 2 (22 de enero de 2010): 298–308. http://dx.doi.org/10.1021/ci9004139.
Texto completoYugandhar, K. y M. Michael Gromiha. "Protein–protein binding affinity prediction from amino acid sequence". Bioinformatics 30, n.º 24 (28 de agosto de 2014): 3583–89. http://dx.doi.org/10.1093/bioinformatics/btu580.
Texto completoO'Donnell, Timothy J., Alex Rubinsteyn, Maria Bonsack, Angelika B. Riemer, Uri Laserson y Jeff Hammerbacher. "MHCflurry: Open-Source Class I MHC Binding Affinity Prediction". Cell Systems 7, n.º 1 (julio de 2018): 129–32. http://dx.doi.org/10.1016/j.cels.2018.05.014.
Texto completoFan, Cong, Ping-pui Wong y Huiying Zhao. "DStruBTarget: Integrating Binding Affinity with Structure Similarity for Ligand-Binding Protein Prediction". Journal of Chemical Information and Modeling 60, n.º 1 (13 de diciembre de 2019): 400–409. http://dx.doi.org/10.1021/acs.jcim.9b00717.
Texto completoChen, Zihao, Long Hu, Bao-Ting Zhang, Aiping Lu, Yaofeng Wang, Yuanyuan Yu y Ge Zhang. "Artificial Intelligence in Aptamer–Target Binding Prediction". International Journal of Molecular Sciences 22, n.º 7 (30 de marzo de 2021): 3605. http://dx.doi.org/10.3390/ijms22073605.
Texto completoZeng, Haoyang y David K. Gifford. "DeepLigand: accurate prediction of MHC class I ligands using peptide embedding". Bioinformatics 35, n.º 14 (julio de 2019): i278—i283. http://dx.doi.org/10.1093/bioinformatics/btz330.
Texto completoLi, Min, Zhangli Lu, Yifan Wu y YaoHang Li. "BACPI: a bi-directional attention neural network for compound–protein interaction and binding affinity prediction". Bioinformatics 38, n.º 7 (19 de enero de 2022): 1995–2002. http://dx.doi.org/10.1093/bioinformatics/btac035.
Texto completoBae, Haelee y Hojung Nam. "GraphATT-DTA: Attention-Based Novel Representation of Interaction to Predict Drug-Target Binding Affinity". Biomedicines 11, n.º 1 (27 de diciembre de 2022): 67. http://dx.doi.org/10.3390/biomedicines11010067.
Texto completoZhang, Xianfeng, Yanhui Gu, Guandong Xu, Yafei Li, Jinlan Wang y Zhenglu Yang. "HaPPy: Harnessing the Wisdom from Multi-Perspective Graphs for Protein-Ligand Binding Affinity Prediction (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 13 (26 de junio de 2023): 16384–85. http://dx.doi.org/10.1609/aaai.v37i13.27052.
Texto completoAnnala, Matti, Kirsti Laurila, Harri Lähdesmäki y Matti Nykter. "A Linear Model for Transcription Factor Binding Affinity Prediction in Protein Binding Microarrays". PLoS ONE 6, n.º 5 (26 de mayo de 2011): e20059. http://dx.doi.org/10.1371/journal.pone.0020059.
Texto completoZhao, Huiying, Yuedong Yang, Mark von Itzstein y Yaoqi Zhou. "Carbohydrate-binding protein identification by coupling structural similarity searching with binding affinity prediction". Journal of Computational Chemistry 35, n.º 30 (15 de septiembre de 2014): 2177–83. http://dx.doi.org/10.1002/jcc.23730.
Texto completoStrack, Rita. "Predicting RNA–protein binding affinity". Nature Methods 16, n.º 6 (30 de mayo de 2019): 460. http://dx.doi.org/10.1038/s41592-019-0445-4.
Texto completoGhimire, Ashutosh, Hilal Tayara, Zhenyu Xuan y Kil To Chong. "CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention". International Journal of Molecular Sciences 23, n.º 15 (30 de julio de 2022): 8453. http://dx.doi.org/10.3390/ijms23158453.
Texto completoWang, Debby D., Moon-Tong Chan y Hong Yan. "Structure-based protein–ligand interaction fingerprints for binding affinity prediction". Computational and Structural Biotechnology Journal 19 (2021): 6291–300. http://dx.doi.org/10.1016/j.csbj.2021.11.018.
Texto completoHanai, Toshihiko, A. Koseki, R. Yoshikawa, M. Ueno, T. Kinoshita y H. Homma. "Prediction of human serum albumin–drug binding affinity without albumin". Analytica Chimica Acta 454, n.º 1 (marzo de 2002): 101–8. http://dx.doi.org/10.1016/s0003-2670(01)01515-x.
Texto completoZhu, Fangqiang, Xiaohua Zhang, Jonathan E. Allen, Derek Jones y Felice C. Lightstone. "Binding Affinity Prediction by Pairwise Function Based on Neural Network". Journal of Chemical Information and Modeling 60, n.º 6 (27 de abril de 2020): 2766–72. http://dx.doi.org/10.1021/acs.jcim.0c00026.
Texto completoRizzi, Andrea, Steven Murkli, John N. McNeill, Wei Yao, Matthew Sullivan, Michael K. Gilson, Michael W. Chiu et al. "Overview of the SAMPL6 host–guest binding affinity prediction challenge". Journal of Computer-Aided Molecular Design 32, n.º 10 (octubre de 2018): 937–63. http://dx.doi.org/10.1007/s10822-018-0170-6.
Texto completoSuri, Sadhana y Sivanesan Dakshanamurthy. "IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method". Vaccines 10, n.º 10 (8 de octubre de 2022): 1678. http://dx.doi.org/10.3390/vaccines10101678.
Texto completoSharabi, Oz, Jason Shirian y Julia M. Shifman. "Predicting affinity- and specificity-enhancing mutations at protein–protein interfaces". Biochemical Society Transactions 41, n.º 5 (23 de septiembre de 2013): 1166–69. http://dx.doi.org/10.1042/bst20130121.
Texto completoLiang, Yigao, Shaohua Jiang, Min Gao, Fengjiao Jia, Zaoyang Wu y Zhijian Lyu. "GLSTM-DTA: Application of Prediction Improvement Model Based on GNN and LSTM". Journal of Physics: Conference Series 2219, n.º 1 (1 de abril de 2022): 012008. http://dx.doi.org/10.1088/1742-6596/2219/1/012008.
Texto completoZhao, Huiying, Yuedong Yang y Yaoqi Zhou. "Highly accurate and high-resolution function prediction of RNA binding proteins by fold recognition and binding affinity prediction". RNA Biology 8, n.º 6 (noviembre de 2011): 988–96. http://dx.doi.org/10.4161/rna.8.6.17813.
Texto completoFeng, Peiyuan, Jianyang Zeng y Jianzhu Ma. "Predicting MHC-peptide binding affinity by differential boundary tree". Bioinformatics 37, Supplement_1 (1 de julio de 2021): i254—i261. http://dx.doi.org/10.1093/bioinformatics/btab312.
Texto completoFedyushkina, I. V., V. S. Skvortsov, I. V. Romero Reyes y I. S. Levina. "Molecular docking and 3D-QSAR on 16a,17a-cycloalkanoprogesterone analogues as progesterone receptor ligands". Biomeditsinskaya Khimiya 59, n.º 6 (2013): 622–35. http://dx.doi.org/10.18097/pbmc20135906622.
Texto completoMoshari, Mahshad, Qian Wang, Marek Michalak, Mariusz Klobukowski y Jack Adam Tuszynski. "Computational Prediction and Experimental Validation of the Unique Molecular Mode of Action of Scoulerine". Molecules 27, n.º 13 (21 de junio de 2022): 3991. http://dx.doi.org/10.3390/molecules27133991.
Texto completoLiu, Yang, Xia-hui Ouyang, Zhi-Xiong Xiao, Le Zhang y Yang Cao. "A Review on the Methods of Peptide-MHC Binding Prediction". Current Bioinformatics 15, n.º 8 (1 de enero de 2021): 878–88. http://dx.doi.org/10.2174/1574893615999200429122801.
Texto completoLi, Zhongyan, Qingqing Miao, Fugang Yan, Yang Meng y Peng Zhou. "Machine Learning in Quantitative Protein–peptide Affinity Prediction: Implications for Therapeutic Peptide Design". Current Drug Metabolism 20, n.º 3 (22 de mayo de 2019): 170–76. http://dx.doi.org/10.2174/1389200219666181012151944.
Texto completoAgostino, Mark y Sebastian Öther-Gee Pohl. "Wnt Binding Affinity Prediction for Putative Frizzled-Type Cysteine-Rich Domains". International Journal of Molecular Sciences 20, n.º 17 (26 de agosto de 2019): 4168. http://dx.doi.org/10.3390/ijms20174168.
Texto completoYuan, Hong, Jing Huang y Jin Li. "Protein-ligand binding affinity prediction model based on graph attention network". Mathematical Biosciences and Engineering 18, n.º 6 (2021): 9148–62. http://dx.doi.org/10.3934/mbe.2021451.
Texto completoAgrawal, Piyush, Pawan Kumar Raghav, Sherry Bhalla, Neelam Sharma y Gajendra P. S. Raghava. "Overview of Free Software Developed for Designing Drugs Based on Protein-Small Molecules Interaction". Current Topics in Medicinal Chemistry 18, n.º 13 (4 de octubre de 2018): 1146–67. http://dx.doi.org/10.2174/1568026618666180816155131.
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