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GANsDTA: Predicting Drug-Target Binding Affinity Using GANs [PDF]

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   +4 more sources

ImageDTA: A Simple Model for Drug–Target Binding Affinity Prediction [PDF]

ACS Omega
Predicting the drug-target binding affinity (DTA) is crucial in drug discovery, and an increasing number of researchers are using artificial intelligence techniques to make such predictions. Many effective deep neural network prediction models have been proposed. However, current methods need improvement in accuracy, complexity, and efficiency. In this
Li Han, Ling Kang, Quan Guo
doaj   +4 more sources

DGDTA: dynamic graph attention network for predicting drug–target binding affinity [PDF]

BMC Bioinformatics, 2023
Background Obtaining accurate drug–target binding affinity (DTA) information is significant for drug discovery and drug repositioning. Although some methods have been proposed for predicting DTA, the features of proteins and drugs still need to be ...
Haixia Zhai, Hongli Hou, Junwei Luo, Xiaoyan Liu, Zhengjiang Wu, Junfeng Wang   +5 more
doaj   +4 more sources

DrugForm-DTA: Towards real-world drug-target binding affinity model [PDF]

Computational and Structural Biotechnology Journal
Drug-target affinity (DTA) prediction is a fundamental challenge in drug discovery. Computational methods for predicting DTA can greatly assist drug design by narrowing the search space and reducing the number of protein-ligand complexes with low ...
Ivan Khokhlov, Anna Tashchilova, Nikolai Bugaev-Makarovskiy, Olga Glushkova, Vladimir Yudin, Anton Keskinov, Sergey Yudin, Dmitry Svetlichnyy, Veronika Skvortsova   +8 more
doaj   +4 more sources

Drug-target binding affinity prediction based on power graph and word2vec [PDF]

BMC Medical Genomics
Background Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development.
Jing Hu, Shuo Hu, Minghao Xia, Kangxing Zheng, Xiaolong Zhang   +4 more
doaj   +4 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   +5 more sources

InceptionDTA: Predicting drug-target binding affinity with biological context features and inception networks [PDF]

Heliyon
Predicting drug-target binding affinity via in silico methods is crucial in drug discovery. Traditional machine learning relies on manually engineered features from limited data, leading to suboptimal performance.
Mahmood Kalemati, Mojtaba Zamani Emani, Somayyeh Koohi   +2 more
doaj   +4 more sources

A dual-branch graph neural network architecture for drug-target binding affinity prediction [PDF]

Scientific Reports
Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches.
Khushnood Abbas, Chen Hao, Shi Dong, Wengang Zhou, Yong Tang, Shayla Islam, Mohammad Kamrul Hasan, Siti Norul Huda Sheikh Abdullah, Ruizhe Hou   +8 more
doaj   +4 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   +3 more sources

DeepMHADTA: Prediction of Drug-Target Binding Affinity Using Multi-Head Self-Attention and Convolutional Neural Network [PDF]

Current Issues in Molecular Biology, 2022
Drug-target interactions provide insight into the drug-side effects and drug repositioning. However, wet-lab biochemical experiments are time-consuming and labor-intensive, and are insufficient to meet the pressing demand for drug research and ...
Lei Deng, Yunyun Zeng, Hui Liu, Zixuan Liu, Xuejun Liu   +4 more
doaj   +2 more sources