Results 21 to 30 of about 141,734 (283)

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

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

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   +3 more sources

Structure-free drug–target affinity prediction using protein and molecule language models [PDF]

Journal of Cheminformatics
Accurate prediction of drug-target affinity (DTA) is crucial for advancing drug discovery and optimizing experimental processes. Traditional DTA models often rely on handcrafted features or structural data, which can limit their generalizability and ...
Amir Hallaji Bidgoli, Morteza Mahdavi, Hamed Malek   +2 more
doaj   +2 more sources

A meta learning and task adaptive approach for drug target affinity prediction [PDF]

Nature Communications
Accurate and robust prediction of drug-target affinity (DTA) plays a critical role in drug discovery. While deep learning has advanced DTA prediction, existing methods struggle with limited training data and poor generalization. In this study, we propose
Mengxuan Wan, Yanpeng Zhao, Yixin Zhang, Huiyan Xu, Duoyun Yi, Peng Zan, Song He, Xiaochen Bo   +7 more
doaj   +2 more sources

Prediction of Drug-Target Affinity Using Attention Neural Network

International Journal of Molecular Sciences
Studying drug-target interactions (DTIs) is the foundational and crucial phase in drug discovery. Biochemical experiments, while being the most reliable method for determining drug-target affinity (DTA), are time-consuming and costly, making it challenging to meet the current demands for swift and efficient drug development. Consequently, computational
Xin Tang, Xiujuan Lei, Yuchen Zhang
openaire   +3 more sources

GANsDTA: Predicting Drug-Target Binding Affinity Using GANs

Frontiers in Genetics, 2020
The computational prediction of interactions between drugs and targets is a standing challenge in drug discovery. State-of-the-art methods for drug-target interaction prediction are primarily based on supervised machine learning with known label ...
Lingling Zhao, Junjie Wang, Long Pang, Yang Liu, Jun Zhang   +4 more
doaj   +3 more sources

Enhanced information cross-attention fusion for drug–target binding affinity prediction [PDF]

PeerJ Computer Science
Background The rapid development of artificial intelligence has permeated many fields, with its application in drug discovery becoming increasingly mature.
Ailu Fei, Yihan Wang, Tiantian Ruan, Yekang Zhang, Min Yao, Li Wang   +5 more
doaj   +3 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

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

Associative learning mechanism for drug‐target interaction prediction

CAAI Transactions on Intelligence Technology, 2023
As a necessary process of modern drug development, finding a drug compound that can selectively bind to a specific protein is highly challenging and costly.
Zhiqin Zhu, Zheng Yao, Guanqiu Qi, Neal Mazur, Pan Yang, Baisen Cong   +5 more
doaj   +2 more sources