Results 21 to 30 of about 400,933 (233)

GramSeq-DTA: A Grammar-Based Drug–Target Affinity Prediction Approach Fusing Gene Expression Information [PDF]

Biomolecules
Drug–target affinity (DTA) prediction is a critical aspect of drug discovery. The meaningful representation of drugs and targets is crucial for accurate prediction. Using 1D string-based representations for drugs and targets is a common approach that has
Kusal Debnath, Pratip Rana, Preetam Ghosh   +2 more
doaj   +2 more sources

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

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

A geometric graph-based deep learning model for drug-target affinity prediction [PDF]

BMC Bioinformatics
In structure-based drug design, accurately estimating the binding affinity between a candidate ligand and its protein receptor is a central challenge.
Md Masud Rana, Farjana Tasnim Mukta, Duc D. Nguyen   +2 more
doaj   +2 more sources

MDNN-DTA: a multimodal deep neural network for drug-target affinity prediction [PDF]

Frontiers in Genetics
Determining drug-target affinity (DTA) is a pivotal step in drug discovery, where in silico methods can significantly improve efficiency and reduce costs.
Xu Gao, Xu Gao, Mengfan Yan, Mengfan Yan, Chengwei Zhang, Chengwei Zhang, Gang Wu, Gang Wu, Jiandong Shang, Jiandong Shang, Congxiang Zhang, Congxiang Zhang, Kecheng Yang, Kecheng Yang   +13 more
doaj   +2 more sources

DeepDTAGen: a multitask deep learning framework for drug-target affinity prediction and target-aware drugs generation [PDF]

Nature Communications
Identifying novel drugs that can interact with target proteins is a highly challenging, time-consuming, and costly task in drug discovery and development. Numerous machine learning-based models have recently been utilized to accelerate the drug discovery
Pir Masoom Shah, Huimin Zhu, Zhangli Lu, Kaili Wang, Jing Tang, Min Li   +5 more
doaj   +2 more sources

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

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

Drug–Target Affinity Prediction Based on Cross-Modal Fusion of Text and Graph

Applied Sciences
Drug–target affinity (DTA) prediction is a critical step in virtual screening and significantly accelerates drug development. However, existing deep learning-based methods relying on single-modal representations (e.g., text or graphs) struggle to fully ...
Jucheng Yang, Fushun Ren
doaj   +2 more sources

SMFF-DTA: using a sequential multi-feature fusion method with multiple attention mechanisms to predict drug-target binding affinity

BMC Biology
Background Drug-target binding affinity (DTA) prediction can accelerate the drug screening process, and deep learning techniques have been used in all facets of drug research.
Xun Wang, Zhijun Xia, Runqiu Feng, Tongyu Han, Hanyu Wang, Wenqian Yu, Xingguang Wang   +6 more
doaj   +2 more sources

SAG-DTA: Prediction of Drug-Target Affinity Using Self-Attention Graph Network. [PDF]

Int J Mol Sci, 2021
The prediction of drug–target affinity (DTA) is a crucial step for drug screening and discovery. In this study, a new graph-based prediction model named SAG-DTA (self-attention graph drug–target affinity) was implemented.
Zhang S, Jiang M, Wang S, Wang X, Wei Z, Li Z.   +5 more
europepmc   +2 more sources

Explainable deep drug–target representations for binding affinity prediction

BMC Bioinformatics, 2022
Background Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug–target interactions and new leads. However, most of these methodologies have been overlooking the importance of providing
Nelson R. C. Monteiro, Carlos J. V. Simões, Henrique V. Ávila, Maryam Abbasi, José L. Oliveira, Joel P. Arrais   +5 more
doaj   +1 more source