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Multimodal contrastive representation learning for drug-target binding affinity prediction
Methods, 2023In the biomedical field, the efficacy of most drugs is demonstrated by their interactions with targets, meanwhile, accurate prediction of the strength of drug-target binding is extremely important for drug development efforts. Traditional bioassay-based drug-target binding affinity (DTA) prediction methods cannot meet the needs of drug R&D in the era ...
Linlin Zhang +2 more
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Drug-target continuous binding affinity prediction using multiple sources of information
Expert Systems With Applications, 2021Abstract Drug-target binding affinity prediction has a significant role in the search for new drugs or novel targets for existing drugs. The vast majority of recent computational approaches, presented for the task of drug-target binding affinity prediction, make use of a single source to measure drug-drug or protein-protein similarities ...
Betsabeh Tanoori +2 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.
Yuanyuan Chen
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Prediction of drug–target binding affinity based on multi-scale feature fusion
Computers in Biology and MedicineAccurate prediction of drug-target binding affinity (DTA) plays a pivotal role in drug discovery and repositioning. Although deep learning methods are widely used in DTA prediction, two significant challenges persist: (i) how to effectively represent the complex structural information of proteins and drugs; (ii) how to precisely model the mutual ...
Hui Yu, Tian Tan, Zun Liu
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A Mutual Attention Model for Drug Target Binding Affinity Prediction
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2022Vrious machine learning approaches have been developed for drug-target interaction (DTI) prediction. One class of these approaches, DTBA, is interested in Drug-Target Binding Affinity strength, rather than focusing merely on the presence or absence of interaction. Several machine learning methods have been developed for this purpose.
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AttentionDTA: prediction of drug–target binding affinity using attention model
2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019In bioinformatics, machine learning-based prediction of drug-target interaction (DTI) plays an important role in virtual screening of drug discovery. DTI prediction, which have been treated as a binary classification problem, depends on the concentration of two molecules, the interaction between two molecules, and other factors.
Qichang Zhao +4 more
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Modelling Drug-Target Binding Affinity using a BERT based Graph Neural network
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2021Understanding the interactions between novel drugs and target proteins is fundamentally important in disease research as discovering drug-protein interactions can be an exceptionally time-consuming and expensive process. Alternatively, this process can be simulated using modern deep learning methods that have the potential of utilising vast quantities ...
Lennox, Mark +2 more
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