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GFLearn: Generalized Feature Learning for Drug-Target Binding Affinity Prediction
IEEE Journal of Biomedical and Health InformaticsPredicting drug-target binding affinity is critical for drug discovery, as it helps identify promising drug candidates and predict their effectiveness. Recent advancements in deep learning have made significant progress in tackling this task. However, existing methods heavily rely on training data, and their performance is often limited when predicting
Zibo Huang, Xinrui Weng, Le Ou-Yang
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Small-molecule affinity chromatography coupled mass spectrometry for drug target deconvolution
Expert Opinion on Drug Discovery, 2009Current drug discovery organizations have renewed interest in phenotypic/function based screening for the identification of novel small-molecule drug candidates. Phenotypic screening faces the challenge of deconvoluting the identity of molecular targets of small-molecules through which they exert their biological effect.
Chaitanya, Saxena +3 more
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GEFA: EARLY FUSION APPROACH IN DRUG-TARGET AFFINITY PREDICTION
Journal of Engineering SciencesPredicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA) problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues.
null B.VASANTHA +3 more
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Drug-Target Affinity Prediction Based on Topological Enhanced Graph Neural Networks
Journal of Chemical Information and ModelingGraph neural networks (GNNs) have achieved remarkable success in drug-target affinity (DTA) analysis, reducing the cost of drug development. Unlike traditional one-dimensional (1D) sequence-based methods, GNNs leverage graph structures to capture richer protein and drug features, leading to improved DTA prediction performance. However, existing methods
Hengliang Guo +11 more
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Drug-Target Affinity Prediction Based on Improved GraphDTA
2023 5th International Conference on Robotics and Computer Vision (ICRCV), 2023Zi Ye +3 more
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MultiKD-DTA: Enhancing Drug-Target Affinity Prediction Through Multiscale Feature Extraction
Interdisciplinary Sciences: Computational Life SciencesThe discovery and development of novel pharmaceutical agents is characterized by high costs, lengthy timelines, and significant safety concerns. Traditional drug discovery involves pharmacologists manually screening drug molecules against protein targets, focusing on binding within protein cavities.
Riqian Hu +5 more
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MFF-DTA: Multi-scale feature fusion for drug-target affinity prediction
MethodsAccurately predicting drug-target affinity is crucial in expediting the discovery and development of new drugs, which is a complex and risky process. Identifying these interactions not only aids in screening potential compounds but also guides further optimization.
Xiwei Tang +3 more
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Prediction of drug-target binding affinity based on deep learning models
Computers in Biology and MedicineThe prediction of drug-target binding affinity (DTA) plays an important role in drug discovery. Computerized virtual screening techniques have been used for DTA prediction, greatly reducing the time and economic costs of drug discovery. However, these techniques have not succeeded in reversing the low success rate of new drug development.
Hao, Zhang +4 more
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Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction
2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022Mei Li +4 more
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies
Ca-A Cancer Journal for Clinicians, 2022Paolo Tarantino +2 more
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