Results 231 to 240 of about 6,076,909 (260)

Multimodal contrastive representation learning for drug-target binding affinity prediction

Methods, 2023
In 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, Chunping, Ouyang, Yongbin, Liu, Yiming, Liao, Zheng, Gao   +4 more
openaire   +2 more sources

AttentionDTA: prediction of drug–target binding affinity using attention model

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2019
In 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, Fen Xiao, Mengyun Yang, Yaohang Li, Jianxin Wang 0001   +4 more
openaire   +2 more sources

Effective drug-target affinity prediction via generative active learning

Information Sciences
Yuansheng Liu, Xiangxiang Zeng, Dong-Sheng Cao   +2 more
exaly   +2 more sources

Drug-target Affinity Prediction by Molecule Secondary Structure Representation Network

Current Medicinal Chemistry
Introduction: Identification of drug-target interactions (DTI) is a crucial step in drug development with high specificity and low toxicity. To accelerate the process, computer-aided DTI prediction algorithms have been used to screen compounds or targets rapidly. Furthermore, DTI prediction can be used to identify potential targets for existing drugs,
Yuewei, Tang, Yunhai, Li, Pengpai, Li, Zhi-Ping, Liu   +3 more
openaire   +2 more sources

GFLearn: Generalized Feature Learning for Drug-Target Binding Affinity Prediction

IEEE Journal of Biomedical and Health Informatics
Predicting 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
openaire   +2 more sources

Classification prediction of drug target binding affinity based on the MolrProtTrans model

Analytical Biochemistry
Predicting drug-target interactions is essential for virtual drug screening. While many models predict the binding affinity between small molecules and proteins, they often overemphasize molecular features while overlooking important protein characteristics, leading to biased predictions.
Yicun Lin, Yuanfeng Li, Wei Sun, Jian Wang   +3 more
openaire   +2 more sources

Contrastive Meta-Learning for Drug-Target Binding Affinity Prediction

2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022
Mei Li, Sihan Xu, Xiangrui Cai, Zhong Zhang 0001, Hua Ji   +4 more
openaire   +1 more source

Drug-Target Affinity Prediction Based on Improved GraphDTA

2023 5th International Conference on Robotics and Computer Vision (ICRCV), 2023
Zi Ye, Xiangyang Chen, Leiluo Wang, Shukai Jiang   +3 more
openaire   +1 more source

Prediction of drug-target binding affinity based on deep learning models

Computers in Biology and Medicine
The 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, Xiaoqian Liu, Wenya Cheng, Tianshi Wang, Yuanyuan Chen   +4 more
exaly   +4 more sources

Drug-target Affinity Prediction Based on Graph Transformer and Selfattention Mechanism Kinase-specific Drug-target Affinity Prediction with Graph Transformer and Self-Attention Fusion

Current Computer-Aided Drug Design
Introduction/Objective: Traditional drug discovery methods face efficiency bottlenecks in predicting drug-target binding affinity (DTA), particularly for kinase inhibitor screening. This study proposes GTDDTA-a novel deep learning framework based on graph transformers and self-attention mechanisms-to address ...
Shiqian, Han, Jiahao, Shi, Jun, Wang
openaire   +2 more sources