Relevant bibliographies by topics / Prediction of binding affinity

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

Academic literature on the topic 'Prediction of binding affinity'

Author: Grafiati
Published: 6 September 2023
Last updated: 31 July 2025

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Journal articles on the topic "Prediction of binding affinity"

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Kondabala, Rajesh, Vijay Kumar, Amjad Ali, and Manjit Kaur. "A novel astrophysics-based framework for prediction of binding affinity of glucose binder." Modern Physics Letters B 34, no. 31 (2020): 2050346. http://dx.doi.org/10.1142/s0217984920503467.

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In this paper, a novel astrophysics-based prediction framework is developed for estimating the binding affinity of a glucose binder. The proposed framework utilizes the molecule properties for predicting the binding affinity. It also uses the astrophysics-learning strategy that incorporates the concepts of Kepler’s law during the prediction process. The proposed framework is compared with 10 regression algorithms over ZINC dataset. Experimental results reveal that the proposed framework provides 99.30% accuracy of predicting binding affinity. However, decision tree provides the prediction with
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Kwon, Yongbeom, Woong-Hee Shin, Junsu Ko, and Juyong Lee. "AK-Score: Accurate Protein-Ligand Binding Affinity Prediction Using an Ensemble of 3D-Convolutional Neural Networks." International Journal of Molecular Sciences 21, no. 22 (2020): 8424. http://dx.doi.org/10.3390/ijms21228424.

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Accurate prediction of the binding affinity of a protein-ligand complex is essential for efficient and successful rational drug design. Therefore, many binding affinity prediction methods have been developed. In recent years, since deep learning technology has become powerful, it is also implemented to predict affinity. In this work, a new neural network model that predicts the binding affinity of a protein-ligand complex structure is developed. Our model predicts the binding affinity of a complex using the ensemble of multiple independently trained networks that consist of multiple channels o
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Antunes, Dinler A., Jayvee R. Abella, Didier Devaurs, Maurício M. Rigo, and Lydia E. Kavraki. "Structure-based Methods for Binding Mode and Binding Affinity Prediction for Peptide-MHC Complexes." Current Topics in Medicinal Chemistry 18, no. 26 (2019): 2239–55. http://dx.doi.org/10.2174/1568026619666181224101744.

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Understanding the mechanisms involved in the activation of an immune response is essential to many fields in human health, including vaccine development and personalized cancer immunotherapy. A central step in the activation of the adaptive immune response is the recognition, by T-cell lymphocytes, of peptides displayed by a special type of receptor known as Major Histocompatibility Complex (MHC). Considering the key role of MHC receptors in T-cell activation, the computational prediction of peptide binding to MHC has been an important goal for many immunological applications. Sequence- based
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Wang, Debby D., Haoran Xie, and Hong Yan. "Proteo-chemometrics interaction fingerprints of protein–ligand complexes predict binding affinity." Bioinformatics 37, no. 17 (2021): 2570–79. http://dx.doi.org/10.1093/bioinformatics/btab132.

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Abstract Motivation Reliable predictive models of protein–ligand binding affinity are required in many areas of biomedical research. Accurate prediction based on current descriptors or molecular fingerprints (FPs) remains a challenge. We develop novel interaction FPs (IFPs) to encode protein–ligand interactions and use them to improve the prediction. Results Proteo-chemometrics IFPs (PrtCmm IFPs) formed by combining extended connectivity fingerprints (ECFPs) with the proteo-chemometrics concept. Combining PrtCmm IFPs with machine-learning models led to efficient scoring models, which were vali
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Han, Rong, Xiaohong Liu, Tong Pan, et al. "CoPRA: Bridging Cross-domain Pretrained Sequence Models with Complex Structures for Protein-RNA Binding Affinity Prediction." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 1 (2025): 246–54. https://doi.org/10.1609/aaai.v39i1.32001.

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Accurately measuring protein-RNA binding affinity is crucial in many biological processes and drug design. Previous computational methods for protein-RNA binding affinity prediction rely on either sequence or structure features, unable to capture the binding mechanisms comprehensively. The recent emerging pre-trained language models trained on massive unsupervised sequences of protein and RNA have shown strong representation ability for various in-domain downstream tasks, including binding site prediction. However, applying different-domain language models collaboratively for complex-level tas
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Nguyen, Austin, Abhinav Nellore, and Reid F. Thompson. "Discordant results among major histocompatibility complex binding affinity prediction tools." F1000Research 12 (June 7, 2023): 617. http://dx.doi.org/10.12688/f1000research.132538.1.

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Background: Human leukocyte antigen (HLA) alleles are critical components of the immune system’s ability to recognize and eliminate tumors and infections. A large number of machine learning-based major histocompatibility complex (MHC) binding affinity (BA) prediction tools have been developed and are widely used for both investigational and therapeutic applications, so it is important to explore differences in tool outputs. Methods: We examined predictions of four popular tools (netMHCpan, HLAthena, MHCflurry, and MHCnuggets) across a range of possible peptide sources (human, viral, and random
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Shar, Piar Ali, Weiyang Tao, Shuo Gao, et al. "Pred-binding: large-scale protein–ligand binding affinity prediction." Journal of Enzyme Inhibition and Medicinal Chemistry 31, no. 6 (2016): 1443–50. http://dx.doi.org/10.3109/14756366.2016.1144594.

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Husnul, Khotimah, Jatmiko Widy, Nur Azizah Dita та ін. "Prediction of drug candidate from Rosmarinus officinalis L to inhibit IL-6R, IL-1R1, and TNF-α: In silico study". World Journal of Advanced Research and Reviews 21, № 2 (2024): 252–60. https://doi.org/10.5281/zenodo.13995362.

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Tooth pain is a manifestation of pulpitis caused by dental caries. Toothache can be treated using eugenol. However, eugenol has several disadvantages, including its toxic effects on fibroblast pulp tissue in a dose-dependent manner. This research assesses the binding affinity of drug candidates, predicting physicochemical properties, pharmacokinetics, drug-likeness, LD50, and toxicity. Molecular docking results show that Rosmarinic acid, Carnosic acid, Carnosol, Ursolic acid can bind strongly to IL-6R and IL-1R1. Meanwhile, only the compounds Carnosic acid and Ursolic acid bind strongly to TNF
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Wang, Xun, Dayan Liu, Jinfu Zhu, Alfonso Rodriguez-Paton, and 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, no. 5 (2021): 643. http://dx.doi.org/10.3390/biom11050643.

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The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, we propose a novel deep learning method, namely CSConv2d, for protein-ligand interactions’ prediction. The proposed method is improved by a DEEPScreen model using 2-D structural representations of compounds as input. Furthermore, a channel and spatial attention mechanism (CS) is added in feature abstractions. Data experiments conducted on ChEMBLv23
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Langham, James J., Ann E. Cleves, Russell Spitzer, Daniel Kirshner, and Ajay N. Jain. "Physical Binding Pocket Induction for Affinity Prediction." Journal of Medicinal Chemistry 52, no. 19 (2009): 6107–25. http://dx.doi.org/10.1021/jm901096y.

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Dissertations / Theses on the topic "Prediction of binding affinity"

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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/.

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As we move towards an era of personalised medicine, the identification of lead compounds requires years of research and considerable financial backing, in the development of targeted therapies for cancer. We use molecular modelling and simulation to screen a library of active compounds, and understand the ligand-protein interaction at the molecular level in appropriate protein targets, in a bid to identify the most active lead drug candidates. In recent times, good progress has been made in accurately predicting binding affinities for drug candidates. Advances in high-performance computation (
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Uslan, 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.

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Reliable prediction of binding affinity of peptides is one of the most challenging but important complex modelling problems in the post-genome era due to the diversity and functionality of the peptides discovered. Generally, peptide binding prediction models are commonly used to find out whether a binding exists between a certain peptide(s) and a major histocompatibility complex (MHC) molecule(s). Recent research efforts have been focused on quantifying the binding predictions. The objective of this thesis is to develop reliable real-value predictive models through the use of fuzzy systems. A
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Bodnarchuk, 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/.

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In this thesis, various methods for locating and scoring the binding affinity of water molecules and molecular fragments in protein binding sites are described. The primary aim of this work is to understand how different methodologies compare to one another and how, by carefully choosing the correct method, they can be used to extract information on how small molecules interact with proteins. Three different methods are used to predict the location and affinity of water molecules; Just Add Water Molecules (JAWS), Grand Canonical Monte Carlo (GCMC) and double-decoupling. By applying the methods
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Erdas, 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.

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Machine learning methods have been promising tools in science and engineering fields. The use of these methods in chemistry and drug design has advanced after 1990s. In this study, molecular electrostatic potential (MEP) surfaces of PCP-like compounds are modelled and visualized in order to extract features which will be used in predicting binding affinity. In modelling, Cartesian coordinates of MEP surface points are mapped onto a spherical self-organizing map. Resulting maps are visualized by using values of electrostatic potential. These values also provide features for prediction system. S
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Guedes, 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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Submitted by Maria Cristina (library@lncc.br) on 2017-04-12T19:05:59Z No. of bitstreams: 1 tese_isabella_vfinal.pdf: 6145955 bytes, checksum: e3ed369e970ad7eb06b79a77ef921a9b (MD5)<br>Approved for entry into archive by Maria Cristina (library@lncc.br) on 2017-04-12T19:06:11Z (GMT) No. of bitstreams: 1 tese_isabella_vfinal.pdf: 6145955 bytes, checksum: e3ed369e970ad7eb06b79a77ef921a9b (MD5)<br>Made available in DSpace on 2017-04-12T19:06:22Z (GMT). No. of bitstreams: 1 tese_isabella_vfinal.pdf: 6145955 bytes, checksum: e3ed369e970ad7eb06b79a77ef921a9b (MD5) Previous issue date: 2016-07-2
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Matereke, Lavious Tapiwa. "Analysis of predictive power of binding affinity of PBM-derived sequences." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018666.

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A transcription factor (TF) is a protein that binds to specific DNA sequences as part of the initiation stage of transcription. Various methods of finding these transcription factor binding sites (TFBS) have been developed. In vivo technologies analyze DNA binding regions known to have bound to a TF in a living cell. Most widely used in vivo methods at the moment are chromatin immunoprecipitation followed by deep sequencing (ChIP-seq) and DNase I hypersensitive sites sequencing. In vitro methods derive TFBS based on experiments with TFs and DNA usually in artificial settings or computationally
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Yoldas, 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.

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This study aims to predict the binding affinity of the PCP-like compounds by means of molecular hydrophobicity. Molecular hydrophobicity is an important property which affects the binding affinity of molecules. The values of molecular hydrophobicity of molecules are obtained on three-dimensional coordinate system. Our aim is to reduce the number of points on the hydrophobicity surface of the molecules. This is modeled by using self organizing maps (SOM) and k-means clustering. The feature sets obtained from SOM and k-means clustering are used in order to predict binding affinity of molecules i
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Shoemake, 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.

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Atkovska, Kalina, Sergey A. Samsonov, Maciej Paszkowski-Rogacz, and 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.

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Molecular docking has been extensively applied in virtual screening of small molecule libraries for lead identification and optimization. A necessary prerequisite for successful differentiation between active and non-active ligands is the accurate prediction of their binding affinities in the complex by use of docking scoring functions. However, many studies have shown rather poor correlations between docking scores and experimental binding affinities. Our work aimed to improve this correlation by implementing a multipose binding concept in the docking scoring scheme. Multipose binding, i.e.,
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Nordesjö, 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.

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Greater understanding of how we can use protein simulations and statistical characteristics of biomolecular interfaces as proxies for biological function will make manifest major advances in protein engineering. Here we show how to use calculated change in binding affinity and coevolutionary scores to predict the functional effect of mutations in the interface between a Histidine Kinase and a Response Regulator. These proteins participate in the Two-Component Regulatory system, a system for intracellular signalling found in bacteria. We find that both scores work as proxies for functional muta
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Books on the topic "Prediction of binding affinity"

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Rastogi, Chaitanya. Accurate and Sensitive Quantification of Protein-DNA Binding Affinity. [publisher not identified], 2017.

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Naples, Mark. Determinants of high affinity ligand binding to the group III metabotropic glutamate receptors. National Library of Canada, 2001.

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1958-, McMahon Robert Joseph, ed. Avidin-biotin interactions: Methods and applications. Humana, 2008.

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Marles, Jennifer Anne. Significance of the ligand-binding affinity of the Sho1 SH3 domain for in vivo function. National Library of Canada, 2003.

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1958-, McMahon Robert Joseph, ed. Avidin-biotin interactions: Methods and applications. Humana, 2008.

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Mantovaara, Tuula. The use of calcium (II) and cobalt (II) as adsorbents in immobilized metal ion affinity purification. Uppsala University, 1990.

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Puvvada, 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. University of Portsmouth, Division of Medicinal Chemistry, 1995.

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Marelius, John. Computational Prediction of Receptor-Ligand Binding Affinity in Drug Discovery. Uppsala Universitet, 2000.

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Affinity and Efficacy. World Scientific Publishing Company, 2011.

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Verotoxin-globotriosylceramide binding: Receptor affinity purification and the effect of membrane environment on toxin binding. National Library of Canada, 1993.

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Book chapters on the topic "Prediction of binding affinity"

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Takaba, Kenichiro. "Application of FMO for Protein–ligand Binding Affinity Prediction." In Recent Advances of the Fragment Molecular Orbital Method. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9235-5_13.

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Lu, Yaoyao, Junkai Liu, Tengsheng Jiang, Shixuan Guan, and Hongjie Wu. "Protein-Ligand Binding Affinity Prediction Based on Deep Learning." In Intelligent Computing Theories and Application. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13829-4_26.

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Asha, P. R., and M. S. Vijaya. "Binding Affinity Prediction Models for Spinocerebellar Ataxia Using Supervised Learning." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1423-0_17.

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Liu, Wen, Ji Wan, Xiangshan Meng, Darren R. Flower, and Tongbin Li. "In Silico Prediction of Peptide-MHC Binding Affinity Using SVRMHC." In Methods in Molecular Biology. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-60327-118-9_20.

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Li, Xueling, Min Zhu, Xiaolai Li, Hong-Qiang Wang, and Shulin Wang. "Protein-Protein Binding Affinity Prediction Based on an SVR Ensemble." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31588-6_19.

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Xie, Zhiqi, Zipeng Fan, Peng Zhang, and Qianxi Lin. "CroMamba-DTA: Cross-Mamba for Drug-Target Binding Affinity Prediction." In Lecture Notes in Computer Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-95-0027-7_37.

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Bogdanova, Elizaveta A., Valery N. Novoseletsky, and Konstantin V. Shaitan. "Binding Affinity Prediction in Protein-Protein Complexes Using Convolutional Neural Network." In Advances in Neural Computation, Machine Learning, and Cognitive Research VII. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44865-2_42.

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Nikam, Rahul, K. Yugandhar, and M. Michael Gromiha. "Discrimination and Prediction of Protein-Protein Binding Affinity Using Deep Learning Approach." In Intelligent Computing Theories and Application. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95933-7_89.

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Xia, Minghao, Jing Hu, Xiaolong Zhang, and Xiaoli Lin. "Drug-Target Binding Affinity Prediction Based on Graph Neural Networks and Word2vec." In Intelligent Computing Theories and Application. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13829-4_43.

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Azzopardi, Joseph, and Jean Paul Ebejer. "LigityScore: A CNN-Based Method for Binding Affinity Predictions." In Biomedical Engineering Systems and Technologies. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-20664-1_2.

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Conference papers on the topic "Prediction of binding affinity"

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Rose, Tyler, Charlotte Zhou, and Nicolò Monti. "AffinityLM: Binding-Site Informed Multitask Language Model for Drug-Target Affinity Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822722.

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Wu, Yulong, Jin Xie, Jing Nie, Xiaohong Zhang, and Yuansong Zeng. "Mamba-DTA: Drug-Target Binding Affinity Prediction with State Space Model." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822594.

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Tu, Xinyi, Zhe Li, and Wenbin Lin. "SE-DTA: A Spatial Equivariant Network for Drug-Target Binding Affinity Prediction." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743565.

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Wu, Yulong, Jin Xie, Jing Nie, Jian Hu, and Yuansong Zeng. "Dual Interaction and Kernel-Diverse Network for Accurate Drug-Target Binding Affinity Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822466.

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Prasika, L., Karish Prajaishma G R, and Yoga Vardhani M. "Improved Molecular Binding Affinity Prediction using Deep Learning through Integration of Chemical Structure." In 2024 International Conference on Control, Computing, Communication and Materials (ICCCCM). IEEE, 2024. https://doi.org/10.1109/iccccm61016.2024.11039915.

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Zheng, Jiangbin, Qianhui Xu, Ruichen Xia, and Stan Z. Li. "DapPep: Domain Adaptive Peptide-agnostic Learning for Universal T-cell Receptor-antigen Binding Affinity Prediction." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888436.

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Seo, Sangmin, Seungyeon Choi, Hwanhee Kim, and Sanghyun Park. "PretrainedBA: Enhancing Compound-Protein Binding Affinity Prediction Accuracy via Pre-training Large-Scale Interaction Information." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821938.

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Song, Tao, Siyu Zhang, Xiangyu Meng, Zeyang Zhu, Xianxi Zhu, and Xun Wang. "DCUI-MGraphDTA: Enabling Efficient Inference of a Drug-Target Binding Affinity Prediction Model on DCUs." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10821809.

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Lv, Xing, Weizhong Zhao, Xinhui Tu, and Tingting He. "Predicting Protein-ligand Binding Affinity via Molecular Mechanics-guided Graph Aggregation." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822564.

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Li, Huiting, Weiyu Zhang, Yong Shang, and Wenpeng Lu. "MBC-DTA: A Multi-Scale Bilinear Attention with Contrastive Learning Framework for Drug-Target Binding Affinity Prediction." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822403.

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Reports on the topic "Prediction of binding affinity"

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Warren, H. S. Purification of LPS Binding Factors in Tolerant Serum by Affinity Chromatography. Defense Technical Information Center, 1991. http://dx.doi.org/10.21236/ada233638.

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Reiff, Emily A., and Gunda I. Georg. Construction of Affinity Probes to Study the Epothilone Binding Site on Tubulin. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416670.

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Stratis-Cullum, Dimitra N., Sun McMasters, and Paul M. Pellegrino. Affinity Probe Capillary Electrophoresis Evaluation of Aptamer Binding to Campylobacter jejuni Bacteria. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada512469.

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Fresco, Jacques R. Development of affinity technology for isolating individual human chromosomes by third strand binding. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/820632.

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Pattabiraman, Nagarajan, Carolyn Chambers, Ayesha Adil, and 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. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada612876.

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Chefetz, Benny, Baoshan Xing, and Yona Chen. Interactions of engineered nanoparticles with dissolved organic matter (DOM) and organic contaminants in water. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7699863.bard.

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Background: Engineered carbon nanotubes (CNTs) are expected to be increasingly released into the environment with the rapid increase in their production and use. The discharged CNTs may interact with coexisting contaminants and subsequently change environmental behaviors and ecological effects of both the CNTs themselves and the contaminants. Dissolved organic matter (DOM) plays a critical role in the transport of CNTs in the aquatic environment, affecting both CNT's surface properties through adsorption, and its colloidal stability in solution. Therefore, CNT-bound DOM complexes may interact
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Crocker, Fiona, Lyndsay Carrigee, Kayla Clark, and Karl Indest. Peptide display for rare earth element binding. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49647.

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Rare earth elements (REEs) are metals that are indispensable to the function of many advanced systems and materials. The supply chain of REEs is heavily dependent on foreign sources and supply shortages are a major concern to the US government. Biological recovery approaches could be an economically feasible approach to recover REEs from unconventional or secondary sources. The objective of this project was to express a lanthanide-binding tag, with an affinity for adsorption of REEs, on the surface of the biomining bacterium, Acidithiobacillus ferrooxidans. This was to be accomplished using sy
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Honzatko, Richard, Scott Nelson, Manvi Kapur, Shatabdi Sen та Olivia Gray. Structural Basis for Differential Affinity and Competitive Binding of DNA Polymerase Peptides to the Mycobacterial β-Clamp. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/researchhub.ol79cdw8.1.

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9

Honzatko, Richard, Scott Nelson, Manvi Kapur, Shatabdi Sen та Olivia Gray. Structural Basis for Differential Affinity and Competitive Binding of DNA Polymerase Peptides to the Mycobacterial β-Clamp. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.cqzht3xe.2.

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Nelson, Scott, Manvi Kapur, Olivia J. Gray, Richard B. Honzatko та Shatabdi Sen. Structural Basis for Differential Affinity and Competitive Binding of DNA Polymerase Peptides to the Mycobacterial β-Clamp. ResearchHub Technologies, Inc., 2025. https://doi.org/10.55277/rhj.cqzht3xe.1.

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