Relevant bibliographies by topics / Drug-target interactions

Contents

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

Academic literature on the topic 'Drug-target interactions'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Drug-target interactions"

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Kim, Shinhyuk, Daeyong Jin, and Hyunju Lee. "Predicting Drug-Target Interactions Using Drug-Drug Interactions." PLoS ONE 8, no. 11 (2013): e80129. http://dx.doi.org/10.1371/journal.pone.0080129.

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&NA;. "Predicting off-target drug interactions." Reactions Weekly &NA;, no. 1415 (2012): 2. http://dx.doi.org/10.2165/00128415-201214150-00002.

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Chen, Yuqi, Xiaomin Liang, Wei Du, Yanchun Liang, Garry Wong, and Liang Chen. "Drug–Target Interaction Prediction Based on an Interactive Inference Network." International Journal of Molecular Sciences 25, no. 14 (2024): 7753. http://dx.doi.org/10.3390/ijms25147753.

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Drug–target interactions underlie the actions of chemical substances in medicine. Moreover, drug repurposing can expand use profiles while reducing costs and development time by exploiting potential multi-functional pharmacological properties based upon additional target interactions. Nonetheless, drug repurposing relies on the accurate identification and validation of drug–target interactions (DTIs). In this study, a novel drug–target interaction prediction model was developed. The model, based on an interactive inference network, contains embedding, encoding, interaction, feature extraction,
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GACEM, Hocine, and Amel Ahmane. "Pharmacodynamic drug interactions." Batna Journal of Medical Sciences (BJMS) 1, no. 2 (2014): 96–99. http://dx.doi.org/10.48087/bjmstf.2014.1210.

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Pharmacodynamic interactions are manifested by a change in pharmacological effect at the site of action. There may be a synergistic action, a potentiation or antagonism. Pharmacodynamic interactions are predictable and preventable since the mechanisms of drug action are defined in advance, the interaction drug-target is systematically described and various effectors and signal transduction systems are highlighted. Any time the plurality of molecular targets of the same drug should be considered when prescribing. The wide diffusion of pharmacological information and the updating of knowledge ar
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Yan, Xiao-Ying, Shao-Wu Zhang, and Song-Yao Zhang. "Prediction of drug–target interaction by label propagation with mutual interaction information derived from heterogeneous network." Molecular BioSystems 12, no. 2 (2016): 520–31. http://dx.doi.org/10.1039/c5mb00615e.

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By implementing label propagation on drug/target similarity network with mutual interaction information derived from drug–target heterogeneous network, LPMIHN algorithm identifies potential drug–target interactions.
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Wang, Aizhen, and Minhui Wang. "Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Logistic Matrix Factorization." BioMed Research International 2021 (March 26, 2021): 1–10. http://dx.doi.org/10.1155/2021/5599263.

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Drug-target interactions provide useful information for biomedical drug discovery as well as drug development. However, it is costly and time consuming to find drug-target interactions by experimental methods. As a result, developing computational approaches for this task is necessary and has practical significance. In this study, we establish a novel dual Laplacian graph regularized logistic matrix factorization model for drug-target interaction prediction, referred to as DLGrLMF briefly. Specifically, DLGrLMF regards the task of drug-target interaction prediction as a weighted logistic matri
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Liu, Shuaiqi, Jingjie An, Jie Zhao, Shuhuan Zhao, Hui Lv, and ShuiHua Wang. "Drug-Target Interaction Prediction Based on Multisource Information Weighted Fusion." Contrast Media & Molecular Imaging 2021 (November 24, 2021): 1–10. http://dx.doi.org/10.1155/2021/6044256.

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Recently, in most existing studies, it is assumed that there are no interaction relationships between drugs and targets with unknown interactions. However, unknown interactions mean the relationships between drugs and targets have just not been confirmed. In this paper, samples for which the relationship between drugs and targets has not been determined are considered unlabeled. A weighted fusion method of multisource information is proposed to screen drug-target interactions. Firstly, some drug-target pairs which may have interactions are selected. Secondly, the selected drug-target pairs are
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Lin, Xiaoli, Shuai Xu, Xuan Liu, Xiaolong Zhang, and Jing Hu. "Detecting Drug–Target Interactions with Feature Similarity Fusion and Molecular Graphs." Biology 11, no. 7 (2022): 967. http://dx.doi.org/10.3390/biology11070967.

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The key to drug discovery is the identification of a target and a corresponding drug compound. Effective identification of drug–target interactions facilitates the development of drug discovery. In this paper, drug similarity and target similarity are considered, and graphical representations are used to extract internal structural information and intermolecular interaction information about drugs and targets. First, drug similarity and target similarity are fused using the similarity network fusion (SNF) method. Then, the graph isomorphic network (GIN) is used to extract the features with inf
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Wang, Minhui, Chang Tang, and Jiajia Chen. "Drug-Target Interaction Prediction via Dual Laplacian Graph Regularized Matrix Completion." BioMed Research International 2018 (December 2, 2018): 1–12. http://dx.doi.org/10.1155/2018/1425608.

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Drug-target interactions play an important role for biomedical drug discovery and development. However, it is expensive and time-consuming to accomplish this task by experimental determination. Therefore, developing computational techniques for drug-target interaction prediction is urgent and has practical significance. In this work, we propose an effective computational model of dual Laplacian graph regularized matrix completion, referred to as DLGRMC briefly, to infer the unknown drug-target interactions. Specifically, DLGRMC transforms the task of drug-target interaction prediction into a m
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Ye, Qing, and Ya Xin Sun. "Enhancing Drug-Target Interaction Predictions Using a Divisive Computational Framework." Journal of Biomimetics, Biomaterials and Biomedical Engineering 68 (June 6, 2025): 21–42. https://doi.org/10.4028/p-lxm25a.

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Computational prediction of drug-target interactions (DTIs) is crucial for drug discovery. However, the sparse distribution of DTIs and the imbalance in the number of interactions among targets pose challenges. This study proposes a divisive computational framework. Firstly, it includes a novel preprocessing algorithm that adjusts the interaction matrix based on the number of interactions of a target and its neighbors, enhancing DTI predictions for targets with fewer interactions. Additionally, a new divisive computational testing method is introduced, which evaluates targets with similar numb
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Dissertations / Theses on the topic "Drug-target interactions"

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Vögtli, M. "Nanomechanical detection of drug-target interactions using cantilever sensors." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1307082/.

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The alarming growth of antibiotic-resistant superbugs including methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococci (VRE) is driving the development of new technologies to investigate antibiotics and their modes of action. Novel cantilever array sensors offer a tool to probe the nanomechanics of biomolecular reactions and have recently attracted much attention as a ’label-free’ biosensor as they require no fluorescent or radioactive tags and so biomolecules can be rapidly assayed in a single step reaction. Thereby, cantilever-based sensors are unique in the
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Frank, Annika [Verfasser]. "From natural scaffolds to detailed drug-target interactions on biogenic amines / Annika Frank." Düsseldorf : Universitäts- und Landesbibliothek der Heinrich-Heine-Universität Düsseldorf, 2020. http://d-nb.info/1203872461/34.

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Smith, Adam Joel Taylor. "Computational investigations of enzyme catalysis, design, and conformational aspects of drug-target interactions." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1679378381&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Wang, Chen. "High-throughput prediction and analysis of drug-protein interactions in the druggable human proteome." VCU Scholars Compass, 2018. https://scholarscompass.vcu.edu/etd/5509.

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Drugs exert their (therapeutic) effects via molecular-level interactions with proteins and other biomolecules. Computational prediction of drug-protein interactions plays a significant role in the effort to improve our current and limited knowledge of these interactions. The use of the putative drug-protein interactions could facilitate the discovery of novel applications of drugs, assist in cataloging their targets, and help to explain the details of medicinal efficacy and side-effects of drugs. We investigate current studies related to the computational prediction of drug-protein interaction
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Martínez-Jiménez, Francisco 1988. "Structural study of the therapeutic potential of protein-ligand interactions." Doctoral thesis, Universitat Pompeu Fabra, 2016. http://hdl.handle.net/10803/565402.

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Most of the cellular functions are driven by small-molecules that selectively bind to their protein targets. Is such their importance, that the pharmacological intervention of proteins by small molecule drugs is frequently used to treat multiple conditions. Herein I present a thesis that leverages a threedimensional study of small molecule protein interactions to improve their therapeutic relevance. More specifically, it introduces nAnnolyze, a method for predicting structurally detailed protein-ligand interactions at proteome scale. The method exemplified its applicability by predictin
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Penkler, David Lawrence. "In silico analysis of human Hsp90 for the identification of novel anti-cancer drug target sites and natural compound inhibitors." Thesis, Rhodes University, 2015. http://hdl.handle.net/10962/d1018938.

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The 90-KDa heat shock protein (Hsp90) is part of the molecular chaperone family, and as such it is involved in the regulation of protein homeostasis within cells. Specifically, Hsp90 aids in the folding of nascent proteins and re-folding of denatured proteins. It also plays an important role in the prevention of protein aggregation. Hsp90’s functionality is attributed to its several staged, multi-conformational ATPase cycle, in which associated client proteins are bound and released. Hsp90 is known to be associated with a wide array of client proteins, some of which are thought to be involved
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Coelho, Edgar Duarte de Jesus Valente Marques. "Computational prediction of inter-species protein-protein interactions." Doctoral thesis, Universidade de Aveiro, 2017. http://hdl.handle.net/10773/19165.

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Doutoramento em Ciências da Computação<br>O estudo em larga escala de proteínas e das suas eventuais interações tem sido alvo de bastante atenção pela comunidade científica. Os métodos de análise experimentais têm produzido uma quantidade imensa de dados, que têm sido armazenados em diferentes repositórios. A disponibilidade destes dados, muitos deles curados por especialistas, abre um leque de oportunidades de investigação. Dado que as técnicas experimentais de identificação de interações proteína- proteína (PPIs) são dispendiosas, demoradas e requerem análise de um perito, os métodos comput
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au, low@wehi edu, and CK Andrew Low. "Characterisation and Evaluation of Novel Potential Target (Tubulin) for Antimalarial Chemotherapy." Murdoch University, 2004. http://wwwlib.murdoch.edu.au/adt/browse/view/adt-MU20050930.125714.

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Malaria has long affected the world both socially and economically. Annually, there are 1.5-2.7 million deaths and 300-500 million clinical infections (WHO, 1998). Several antimalarial agents (such as chloroquine, quinine, pyrimethamine, cycloguanil, sulphadoxine and others) have lost their effectiveness against this disease through drug resistance being developed by the malarial parasites (The- Wellcome-Trust, 1999). Although there is no hard-core evidence of drug resistance shown on the new antimalarial compounds (artemisinin and artesunate), induced resistant studies in animal models have d
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Koptelov, Maksim. "Link prediction in bipartite multi-layer networks, with an application to drug-target interaction prediction." Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC211.

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De nombreux problèmes réels relèvent d’une structure bi-relationnelle et peuvent être modélisés suivant des réseaux bipartis. Une telle modélisation permet l'utilisation de solutions standards pour la prédiction et/ou la recommandation de nouvelles relations entre objets de ces réseaux. La tâche de prédiction de liens est un problème largement étudié dans les réseaux simples, c’est-à-dire les réseaux avec un seul type d'interaction entre sommets. Cependant, pour les réseaux multicouche (i.e. réseaux avec plusieurs types d'arêtes entre sommets), ce problème n'est pas encore entièrement résolu.C
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Nadal, Bufi Ferran. "Peptide-based drugs to inhibit LDH5, a potential target for cancer therapy." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/232526/1/Ferran_Nadal%20Bufi_Thesis.pdf.

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This thesis investigates novel strategies to target lactate dehydrogenase 5 (LDH5), a protein involved in cancer. After decades of research without success, this thesis reports the development of the first molecules able to inhibit the activity of LDH5 with an alternative mechanism of action: disrupting its structure. To do that, an emerging class of drugs called peptides are explored. The lead peptide of this work successfully kills breast cancer cells via LDH5 inhibition. The validation of this strategy is relevant because it can be applied to many other cancer targets that have been traditi
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Books on the topic "Drug-target interactions"

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Lambert, David G. Mechanisms and determinants of anaesthetic drug action. Edited by Michel M. R. F. Struys. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0013.

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This chapter is broken into two main sections: a general description of the principles of ligand receptor interaction and a discussion of the main groups of ‘targets’; and explanation of some common pharmacological interactions in anaesthesia, critical care, and pain management. Agonists bind to and activate receptors while antagonists bind to receptors and block the effects of agonists. Antagonists can be competitive (most common) or non-competitive/irreversible. The main classes of drug target are enzymes, carriers, ion channels, and receptors with examples of anaesthetic relevance interacti
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Coghlan, J. Gerry, and Benjamin E. Schreiber. Cardiovascular system. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0019.

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Rheumatology, as a specialty that encounters many multisystem diseases, requires knowledge of many of the more exotic cardiovascular conditions including large- and small-vessel vasculitis, pulmonary hypertension, and myopericarditis. In addition, many rheumatology patients will suffer from cardiovascular pathology due to its common nature and association with an older population, since many previously lethal conditions are now associated with better survival. This requires a detailed knowledge of the drug—drug interactions that arise and the off-target consequences of rheumatological therapie
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Coghlan, J. Gerry, and Benjamin E. Schreiber. Cardiovascular system. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199642489.003.0019_update_002.

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Rheumatology, as a specialty that encounters many multisystem diseases, requires knowledge of many of the more exotic cardiovascular conditions including large- and small-vessel vasculitis, pulmonary hypertension, and myopericarditis. In addition, many rheumatology patients will suffer from cardiovascular pathology due to its common nature and association with an older population, since many previously lethal conditions are now associated with better survival. This requires a detailed knowledge of the drug—drug interactions that arise and the off-target consequences of rheumatological therapie
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Kramer, Carolyn, and Emily Blumberg. Immunosuppressants and Antiretroviral Therapy in HIV-Positive Transplant Patients. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190493097.003.0028.

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Protease inhibitors (PIs), especially ritonavir, are inhibitors of CYP3A4 and P-gp1 and can significantly increase levels of calcineurin inhibitors and mammalian target of rapamycin (mTOR) inhibitors. Cobicistat is an inhibitor of CYP3A4, and its effect on levels of calcineurin inhibitors and mTOR inhibitors is likely to be similar to that of ritonavir. Efavirenz may result in lower concentrations of calcineurin inhibitors and mTOR inhibitors. Dose reduction and careful attention to monitoring drug levels are critical to avoid toxicity and maintain therapeutic immunosuppressive concentrations
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Ansari, Arash, and David Osser. Psychopharmacology. Oxford University Press, 2020. http://dx.doi.org/10.1093/med/9780197537046.001.0001.

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Psychopharmacology: A Concise Overview, 3rd Edition discusses and reviews currently available psychiatric medications and their evidence-supported use in current clinical practice. It discusses the therapeutic uses of antidepressants, anti-anxiety medications, antipsychotics, mood stabilizers, stimulants, and other medications for attention-deficit/hyperactivity disorder (ADHD), as well as medicines for substance use disorders. It reviews the medications’ mechanisms of action, therapeutic effects, potential drug–drug interactions and short- and long-term adverse effects and risks. It includes
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Book chapters on the topic "Drug-target interactions"

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Talibov, Vladimir O., Vaida Linkuvienė, U. Helena Danielson, and Daumantas Matulis. "Kinetic Analysis of Carbonic Anhydrase–Sulfonamide Inhibitor Interactions." In Carbonic Anhydrase as Drug Target. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12780-0_9.

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Chen, Jiyun, Jihong Wang, Xiaodan Wang, Yingyi Du, and Huiyou Chang. "Predicting Drug Target Interactions Based on GBDT." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96136-1_17.

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Zhao, Xinfeng, Qian Li, Jing Wang, Qi Liang, and Jia Quan. "Key Biochemical Aspects of Drug-Target Interactions." In SpringerBriefs in Molecular Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0078-7_4.

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Maggiora, Gerald, and Vijay Gokhale. "Non-Specificity of Drug-Target Interactions – Consequences for Drug Discovery." In Frontiers in Molecular Design and Chemical Information Science - Herman Skolnik Award Symposium 2015: Jürgen Bajorath. American Chemical Society, 2016. http://dx.doi.org/10.1021/bk-2016-1222.ch007.

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Potter, W. Z., J. K. Hsiao, and H. Ågren. "Neurotransmitter Interactions as a Target of Drug Action." In Clinical Pharmacology in Psychiatry. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-74430-3_5.

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Dunlap, Norma, and Donna M. Huryn. "Lead optimization: drug-target interactions and the pharmacophore." In Medicinal Chemistry. Garland Science, 2018. http://dx.doi.org/10.1201/9781315100470-3.

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Horn, Moritz, Franziska Metge, and Martin S. Denzel. "Unbiased Forward Genetic Screening with Chemical Mutagenesis to Uncover Drug–Target Interactions." In Target Identification and Validation in Drug Discovery. Springer New York, 2019. http://dx.doi.org/10.1007/978-1-4939-9145-7_2.

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Roszik, Janos, Gábor Tóth, János Szöllősi, and György Vereb. "Validating Pharmacological Disruption of Protein–Protein Interactions by Acceptor Photobleaching FRET Imaging." In Target Identification and Validation in Drug Discovery. Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-311-4_11.

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Ezzat, Ali, Min Wu, Xiaoli Li, and Chee-Keong Kwoh. "Computational Prediction of Drug-Target Interactions via Ensemble Learning." In Methods in Molecular Biology. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-8955-3_14.

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Nath, Abhigyan, and Radha Chaube. "Mining Chemogenomic Spaces for Prediction of Drug–Target Interactions." In Methods in Molecular Biology. Springer US, 2023. http://dx.doi.org/10.1007/978-1-0716-3441-7_9.

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Conference papers on the topic "Drug-target interactions"

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Prasetyo, Viko Pradana, and Wiwik Anggraeni. "Drug-Target Interactions Prediction Using Stacking Ensemble Learning Approach." In 2024 International Electronics Symposium (IES). IEEE, 2024. http://dx.doi.org/10.1109/ies63037.2024.10665756.

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Prakash, M. Sudharsan, G. Meenakashi, and Margaret Marry T. "Feature Extraction of Drug–Target Interactions Usingmodified Transformer Binding Phase." In 2024 2nd International Conference on Recent Advances in Information Technology for Sustainable Development (ICRAIS). IEEE, 2024. https://doi.org/10.1109/icrais62903.2024.10811734.

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Dong, Jinyu. "SCL-DTI: Semi-Supervised Contrastive Learning for Drug-Target Interactions Prediction." In 2024 International Conference on Electronics and Devices, Computational Science (ICEDCS). IEEE, 2024. https://doi.org/10.1109/icedcs64328.2024.00094.

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Jin, Xu, Maoqiang Xie, Yalou Huang, et al. "Predicting Distant Drug-Target Interactions via a Random Walk Guided Graph Neural Network." In 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2024. https://doi.org/10.1109/bibm62325.2024.10822084.

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Liu, Yao, Xin Wang, Ye Liu, and Dandan Dou. "MuRL-DTI: A Multimodal Feature Fusion Reinforcement Learning Approach for Cold Start in Drug-Target Interactions." In ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025. https://doi.org/10.1109/icassp49660.2025.10888048.

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Simon, Essmily, and Sanjay Bankapur. "Leveraging Pre-Trained Text-To-Text Transfer Transformer Language Model for Accurate Prediction of Drug-Target Interactions." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724551.

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Sathan, Dassen, and Shakuntala Baichoo. "Drug Target Interaction prediction using Variational Quantum classifier." In 2024 International Conference on Next Generation Computing Applications (NextComp). IEEE, 2024. https://doi.org/10.1109/nextcomp63004.2024.10779674.

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Chen, Xinyuan, Mohd Nizam Husen, and Xuxia Huang. "MuFAl: A Universal Drug-Target Interaction Prediction Framework." In 2025 19th International Conference on Ubiquitous Information Management and Communication (IMCOM). IEEE, 2025. https://doi.org/10.1109/imcom64595.2025.10857588.

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Taheri, Maryam, Mohammad Reza Keyvanpour, and Mohadeseh Sadat Mousavi. "Improving Drug-Target Interaction Prediction Using Enhanced Feature Selection." In 2024 15th International Conference on Information and Knowledge Technology (IKT). IEEE, 2024. https://doi.org/10.1109/ikt65497.2024.10892664.

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AbuNasser, Raghad J., Mostafa Z. Ali, Yaser Jararweh, Mustafa Daraghmeh, and Talal Z. Ali. "Large Language Models in Drug Discovery: A Comprehensive Analysis of Drug-Target Interaction Prediction." In 2024 2nd International Conference on Foundation and Large Language Models (FLLM). IEEE, 2024. https://doi.org/10.1109/fllm63129.2024.10852448.

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Reports on the topic "Drug-target interactions"

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List, Markus, Quirin Manz, Judith Bernett, et al. D2.1 Whitepaper on the platform knowledge base and data standards for in silico drug repurposing. REPO4EU, 2024. https://doi.org/10.58647/repo4eu.202400d2.1.

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Computational drug repurposing integrates data from diverse sources, such as sequence databases, GWAS studies, or high-throughput screens. Depending on the original use case or field of research, they vary in availability, timeliness, and compatibility with other data sources. Further, numerous computational tools have been introduced designed to identify active disease modules, indications, or drug-target interactions that use different methods and strategies while not adhering to standard guidelines. Clearing and harmonising the resulting inconsistencies consume essential resources such that
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Yedidia, I., H. Senderowitz, and A. O. Charkowski. Small molecule cocktails designed to impair virulence targets in soft rot Erwinias. United States-Israel Binational Agricultural Research and Development Fund, 2020. http://dx.doi.org/10.32747/2020.8134165.bard.

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Chemical signaling between beneficial or pathogenic bacteria and plants is a central factor in determining the outcome of plant-microbe interactions. Pectobacterium and Dickeya (soft rot Erwinias) are the major cause of soft rot, stem rot, and blackleg formed on potato and ornamentals, currently with no effective control. Our major aim was to establish and study specific bacterial genes/proteins as targets for anti-virulence compounds, by combining drug design tools and bioinformatics with experimental work. The approach allowed us to identify and test compounds (small molecules) that specific
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