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Machine Learning for Drug-Target Interaction Prediction [PDF]

Molecules, 2018
Identifying drug-target interactions will greatly narrow down the scope of search of candidate medications, and thus can serve as the vital first step in drug discovery.
Ruolan Chen, Xiangrong Liu, Shuting Jin, Jiawei Lin, Juan Liu   +4 more
doaj   +5 more sources

In silico methods for drug-target interaction prediction [PDF]

Cell Reports: Methods
Summary: Drug-target interaction (DTI) prediction is a crucial component of drug discovery. In recent years, in silico approaches have attracted attention for DTI prediction, primarily because of their potential to mitigate the high costs, low success ...
Xiaoqing Ru, Lifeng Xu, Wu Han, Quan Zou
doaj   +4 more sources

Drug–Target Interaction Prediction via Dual-Interaction Fusion [PDF]

Molecules
Accurate prediction of drug–target interaction (DTI) is crucial for modern drug discovery. However, experimental assays are costly, and many existing computational models still face challenges in capturing multi-scale features, fusing cross-modal ...
Xingyang Li, Zepeng Li, Bo Wei, Yuni Zeng   +3 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   +3 more sources

Leveraging multimodal learning for enhanced drug-target interaction prediction [PDF]

Frontiers in Pharmacology
IntroductionThe evolving landscape of artificial intelligence in drug discovery necessitates increasingly sophisticated approaches to predict drug-target interactions (DTIs) with high precision and generalizability. In alignment with the current surge of
Guo Chen, Kaixin Sun
doaj   +2 more sources

Evidential deep learning-based drug-target interaction prediction [PDF]

Nature Communications
Drug-target interaction (DTI) prediction is a crucial component of drug discovery. Recent deep learning methods show great potential in this field but also encounter substantial challenges.
Yanpeng Zhao, Yuting Xing, Yixin Zhang, Yifei Wang, Mengxuan Wan, Duoyun Yi, Chengkun Wu, Shangze Li, Huiyan Xu, Hongyang Zhang, Ziyi Liu, Guowei Zhou, Mengfan Li, Xuanze Wang, Zhengshan Chen, Ruijiang Li, Lianlian Wu, Dongsheng Zhao, Peng Zan, Song He, Xiaochen Bo   +20 more
doaj   +2 more sources

A generative framework for enhancing drug target interaction prediction in drug discovery [PDF]

Scientific Reports
In silico drug-target interaction (DTI) prediction plays a key role in accelerating drug discovery and understanding molecular mechanisms. Traditional methods often struggle with the complexity and scale of biochemical data, thus limiting prediction ...
Roshan R. Kotkondawar, Sanjay R. Sutar, Arvind W. Kiwelekar, Vinod J. Kadam, Shivajirao M. Jadhav   +4 more
doaj   +2 more sources

MolTrans: Molecular Interaction Transformer for drug–target interaction prediction [PDF]

Bioinformatics, 2020
Abstract Motivation Drug–target interaction (DTI) prediction is a foundational task for in-silico drug discovery, which is costly and time-consuming due to the need of experimental search over large drug compound space.
Kexin Huang, Cao Xiao, Lucas M. Glass, Jimeng Sun 0001   +3 more
openaire   +3 more sources

Cytokine–receptor interactions as drug targets [PDF]

Current Opinion in Chemical Biology, 2010
Cytokines are essential proteins that exert potent control over entire cell populations to fight infections and other pathologies, but can by themselves cause disease. Therefore, cytokine-related drugs act either by stimulating or blocking their activities.
Schreiber, Gideon, Walter, Mark R
openaire   +2 more sources

Transfer learning for drug–target interaction prediction

Bioinformatics, 2023
Abstract Motivation Utilizing AI-driven approaches for drug–target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between
Alperen Dalkiran, Ahmet Atakan, Ahmet Süreyya Rifaioglu, Maria Jesus Martin, Rengül Çetin-Atalay, Aybar C. Acar, Tunca Dogan, Volkan Atalay   +7 more
openaire   +5 more sources