Results 21 to 30 of about 4,932,082 (261)

KAN-GIN: Adaptive Nonlinear Molecular Representation Learning for Drug-Target Affinity Prediction [PDF]

Pharmaceuticals
Background/Objectives: Accurate drug-target affinity (DTA) prediction supports virtual screening, lead optimization, and drug repurposing. This study investigates whether replacing the conventional post-aggregation multilayer perceptron within a Graph ...
Abla Bedoui, Nithyadevi Duraisamy, Mohammed Cherkaoui   +2 more
doaj   +2 more sources

Affinity2Vec: drug-target binding affinity prediction through representation learning, graph mining, and machine learning

Scientific Reports, 2022
Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual drug screening. Most DTI prediction methods cast the problem as a binary classification task to predict if interactions exist or as a regression task to ...
Maha A. Thafar, Mona Alshahrani, Somayah Albaradei, Takashi Gojobori, Magbubah Essack, Xin Gao   +5 more
doaj   +2 more sources

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

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

MTAF–DTA: multi-type attention fusion network for drug–target affinity prediction [PDF]

BMC Bioinformatics
Background The development of drug–target binding affinity (DTA) prediction tasks significantly drives the drug discovery process forward. Leveraging the rapid advancement of artificial intelligence, DTA prediction tasks have undergone a transformative ...
Jinghong Sun, Han Wang, Jia Mi, Jing Wan, Jingyang Gao   +4 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

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

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

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

Comparison Study of Computational Prediction Tools for Drug-Target Binding Affinities [PDF]

Frontiers in Chemistry, 2019
The drug development is generally arduous, costly, and success rates are low. Thus, the identification of drug-target interactions (DTIs) has become a crucial step in early stages of drug discovery.
Maha Thafar, Maha Thafar, Arwa Bin Raies, Somayah Albaradei, Somayah Albaradei, Magbubah Essack, Vladimir B. Bajic   +6 more
doaj   +4 more sources

Graph-sequence attention and transformer for predicting drug-target affinity. [PDF]

RSC Adv, 2022
We proposed a novel model based on self-attention, called GSATDTA, to predict the binding affinity between drugs and targets. Experimental results show that our model outperforms the state-of-the-art methods on two independent datasets.
Yan X, Liu Y.
europepmc   +3 more sources