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Metapath-aggregated heterogeneous graph neural network for drug-target interaction prediction

Briefings Bioinform., 2023
Drug-target interaction (DTI) prediction is an essential step in drug repositioning. A few graph neural network (GNN)-based methods have been proposed for DTI prediction using heterogeneous biological data.
Mei Li, Xiangrui Cai, Sihan Xu, Hua Ji
semanticscholar   +1 more source

MCANet: shared-weight-based MultiheadCrossAttention network for drug-target interaction prediction

Briefings Bioinform., 2023
Accurate and effective drug-target interaction (DTI) prediction can greatly shorten the drug development lifecycle and reduce the cost of drug development.
Jilong Bian   +4 more
semanticscholar   +1 more source

Protein–Protein Interactions as Drug Targets

Future Medicinal Chemistry, 2015
Modulation of protein-protein interactions (PPIs) is becoming increasingly important in drug discovery and chemical biology. While a few years ago this 'target class' was deemed to be largely undruggable an impressing number of publications and success stories now show that targeting PPIs with small, drug-like molecules indeed is a feasible approach ...
Skwarczynska, Malgorzata   +1 more
openaire   +2 more sources

MHTAN-DTI: Metapath-based hierarchical transformer and attention network for drug-target interaction prediction

Briefings Bioinform., 2023
Drug-target interaction (DTI) prediction can identify novel ligands for specific protein targets, and facilitate the rapid screening of effective new drug candidates to speed up the drug discovery process.
Rang Zhang   +4 more
semanticscholar   +1 more source

DeepMGT-DTI: Transformer network incorporating multilayer graph information for Drug-Target interaction prediction

Comput. Biol. Medicine, 2022
Drug-target interaction (DTI) prediction reduces the cost and time of drug development, and plays a vital role in drug discovery. However, most of research does not fully explore the molecular structures of drug compounds in DTI prediction.
Peiliang Zhang   +3 more
semanticscholar   +1 more source

Drug–Target Interactions

2023
Both the action (pharmacodynamics) and movement (pharmacokinetics) of a drug molecule into, through, and out of the body require some specific or non-specific interactions with target biological molecules. These kinds of interactions could be a combination of electrostatic, hydrogen bonding, dipole, or lipid/hydrophobic interactions in addition to ...
openaire   +1 more source

Learning the Drug-Target Interaction Lexicon

2022
Sequence-based prediction of drug-target interactions has the potential to accelerate drug discovery by complementing experimental screens. Such computational prediction needs to be generalizable and scalable while remaining sensitive to subtle variations in the inputs.
Rohit Singh   +3 more
openaire   +1 more source

Identifying Drug-Target Interactions with Decision Templates

Current Protein & Peptide Science, 2018
During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug ...
Xiao-Ying, Yan, Shao-Wu, Zhang
openaire   +2 more sources

Drugs Targeting Protein—Protein Interactions

ChemInform, 2006
AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 200 leading journals. To access a ChemInform Abstract, please click on HTML or PDF.
openaire   +2 more sources

Drug-Target Interactions: Prediction Methods and Applications

Current Protein & Peptide Science, 2018
Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing.
Shanmugam, Anusuya   +6 more
openaire   +2 more sources

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