Drug-target binding affinity prediction using message passing neural network and self supervised learning [PDF]
BMC Genomics, 2023 Background Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much ...
Leiming Xia +5 more
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Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities [PDF]
Frontiers in Chemistry, 2019 The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery.
Maha Thafar +6 more
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DynHeter-DTA: Dynamic Heterogeneous Graph Representation for Drug-Target Binding Affinity Prediction. [PDF]
Int J Mol SciIn drug development, drug-target affinity (DTA) prediction is a key indicator for assessing the drug’s efficacy and safety. Despite significant progress in deep learning-based affinity prediction approaches in recent years, there are still limitations in capturing the complex interactions between drugs and target receptors.
Li C, Li G.
europepmc +3 more sources
MGraphDTA: deep multiscale graph neural network for explainable drug-target binding affinity prediction. [PDF]
Chem Sci, 2022 MGraphDTA is designed to capture the local and global structure of a compound simultaneously for drug–target affinity prediction and can provide explanations that are consistent with pharmacologists.
Yang Z +3 more
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Deep Drug–Target Binding Affinity Prediction Base on Multiple Feature Extraction and Fusion [PDF]
ACS OmegaZepeng Li +3 more
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Drug-Target Binding Affinity Prediction Using Transformers [PDF]
, 2021 AbstractDrug discovery is generally difficult, expensive, and low success rate. One of the essential steps in the early stages of drug discovery and drug repurposing is identifying drug-target interactions. Binding affinity indicates the strength of drug-target pair interactions.
Mahsa Saadat +3 more
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GraphDTA: Predicting drug–target binding affinity with graph neural networks [PDF]
Bioinformatics, 2019 AbstractThe development of new drugs is costly, time consuming, and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to repurpose drugs effectively, it is useful to know which proteins are targeted by which drugs. Computational
Thin Nguyen +5 more
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Multilevel Attention Models for Drug Target Binding Affinity Prediction [PDF]
Neural Processing Letters, 2021 Drug-Target Binding Affinity (DTBA) prediction is one class of Drug-Target Interaction problem (DTI), where the focus is to predict the binding strength of a drug-target pair. Several machine learning approaches have been developed for this purpose. However, almost all rely on the use of increasingly sophisticated inputs to improve the obtained results
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High-throughput Binding Affinity Calculations at Extreme Scales [PDF]
, 2018 Resistance to chemotherapy and molecularly targeted therapies is a major factor in limiting the effectiveness of cancer treatment. In many cases, resistance can be linked to genetic changes in target proteins, either pre-existing or evolutionarily ...
Balasubramanian, Vivek +7 more
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Optimized hydrophobic interactions and hydrogen bonding at the target-ligand interface leads the pathways of drug-designing. [PDF]
PLoS ONE, 2010 Weak intermolecular interactions such as hydrogen bonding and hydrophobic interactions are key players in stabilizing energetically-favored ligands, in an open conformational environment of protein structures.
Rohan Patil +5 more
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