Results 1 to 10 of about 937,349 (110)

Sequence-based drug-target affinity prediction using weighted graph neural networks

BMC Genomics, 2022
Background Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development.
Mingjian Jiang, Shuang Wang, Shugang Zhang, Wei Zhou, Yuanyuan Zhang, Zhen Li   +5 more
doaj   +2 more sources

Drug–target affinity prediction with extended graph learning-convolutional networks

BMC Bioinformatics
Background High-performance computing plays a pivotal role in computer-aided drug design, a field that holds significant promise in pharmaceutical research.
Haiou Qi, Ting Yu, Wenwen Yu, Chenxi Liu
doaj   +2 more sources

Improving drug–target affinity prediction by adaptive self-supervised learning [PDF]

PeerJ Computer Science
Computational drug-target affinity prediction is important for drug screening and discovery. Currently, self-supervised learning methods face two major challenges in drug-target affinity prediction.
Qing Ye, Yaxin Sun
doaj   +3 more sources

A deep learning method for drug-target affinity prediction based on sequence interaction information mining [PDF]

PeerJ, 2023
Background A critical aspect of in silico drug discovery involves the prediction of drug-target affinity (DTA). Conducting wet lab experiments to determine affinity is both expensive and time-consuming, making it necessary to find alternative approaches.
Mingjian Jiang, Yunchang Shao, Yuanyuan Zhang, Wei Zhou, Shunpeng Pang   +4 more
doaj   +3 more sources

SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features

BMC Bioinformatics, 2023
Background Drug–target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction.
Shourun Pan, Leiming Xia, Lei Xu, Zhen Li   +3 more
doaj   +2 more sources

Graph neural pre-training based drug-target affinity prediction

Frontiers in Genetics
Computational drug-target affinity prediction has the potential to accelerate drug discovery. Currently, pre-training models have achieved significant success in various fields due to their ability to train the model using vast amounts of unlabeled data.
Qing Ye, Yaxin Sun, Yaxin Sun
doaj   +2 more sources

GEFormerDTA: drug target affinity prediction based on transformer graph for early fusion

Scientific Reports
Predicting the interaction affinity between drugs and target proteins is crucial for rapid and accurate drug discovery and repositioning. Therefore, more accurate prediction of DTA has become a key area of research in the field of drug discovery and drug
Youzhi Liu, Linlin Xing, Longbo Zhang, Hongzhen Cai, Maozu Guo   +4 more
doaj   +2 more sources

Learnable protein representations in computational biology for predicting drug-target affinity

Journal of Cheminformatics
In this review, we discuss the various different types of learnable protein representations that have been used in computational biology, with a particular focus on representations that have been used in the paradigm of predicting drug-target affinity ...
Rachit Kumar, Joseph Romano, Marylyn Ritchie   +2 more
doaj   +2 more sources

A comprehensive review of the recent advances on predicting drug-target affinity based on deep learning

Frontiers in Pharmacology
Accurate calculation of drug-target affinity (DTA) is crucial for various applications in the pharmaceutical industry, including drug screening, design, and repurposing.
Xin Zeng, Shu-Juan Li, Shuang-Qing Lv, Meng-Liang Wen, Yi Li   +4 more
doaj   +2 more sources

A Multibranch Neural Network for Drug-Target Affinity Prediction Using Similarity Information

ACS Omega
Predicting drug-target affinity (DTA) is beneficial for accelerating drug discovery. In recent years, graph structure-based deep learning models have garnered significant attention in this field.
Jing Chen, Xiaolin Yang, Haoyu Wu
doaj   +2 more sources