Results 231 to 240 of about 401,503 (258)

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
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

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   +1 more source

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

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

An Efficient Drug Design Method Based on Drug-Target Affinity

2023
Haoran Liu, Xiaolong Zhang 0002, Xiaoli Lin, Jing Hu 0003   +3 more
openaire   +1 more source

LLMDTA: Improving Cold-Start Prediction in Drug-Target Affinity with Biological LLM

IEEE Transactions on Computational Biology and Bioinformatics
Drug-target affinity (DTA) prediction plays a crucial role in accelerating the drug development process. Although deep learning-based models achieve strong performance in benchmark datasets, their predictive accuracy declines sharply in cold-start scenarios, i.e., when encountering drugs or proteins absent from the training set.
Wuguo Tang, Qichang Zhao, Jian-xin Wang 0001   +2 more
openaire   +2 more sources

Co-VAE: Drug-Target Binding Affinity Prediction by Co-Regularized Variational Autoencoders

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022
Tianjiao Li, Xing-Ming Zhao, Limin Li
exaly  

A survey of drug-target interaction and affinity prediction methods via graph neural networks

Computers in Biology and Medicine, 2023
Na Han, Aqing Yang, Hongmin Cai
exaly