Results 271 to 280 of about 2,729,950 (336)

MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction. [PDF]

Molecules
Wang J, He R, Wang X, Li H, Lu Y.
europepmc   +1 more source

Efficient substructure feature encoding based on graph neural network blocks for drug-target interaction prediction. [PDF]

Front Pharmacol
Deng G, Shi C, Ge R, Hu R, Wang C, Qin F, Pan C, Mao H, Yang Q.   +8 more
europepmc   +1 more source

MFCADTI: improving drug-target interaction prediction by integrating multiple feature through cross attention mechanism. [PDF]

BMC Bioinformatics
Quan N, Ma S, Zhao K, Bi X, Zhang L.
europepmc   +1 more source

DHGT-DTI: Advancing drug-target interaction prediction through a dual-view heterogeneous network with GraphSAGE and Graph Transformer. [PDF]

J Pharm Anal
Wang M, Lei X, Guo L, Chen M, Pan Y.
europepmc   +1 more source

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Deep-Learning-Based Drug-Target Interaction Prediction.

Journal of Proteome Research, 2017
Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs.
Ming Wen, Zhimin Zhang, S. Niu, Haozhi Sha, Rui Yang, Yong-Huan Yun, Hongmei Lu   +6 more
semanticscholar   +5 more sources

Drug Target Interaction through Computational Approach

2025 6th International Conference for Emerging Technology (INCET)
The development of effective drugs for diseases requires a comprehensive understanding of the binding affinity between diabetic ligands and target proteins at the molecular level.
Vishal Farande, Padmashree Desai, S. C, Ahm Viswanatha Swamy   +3 more
semanticscholar   +2 more sources

DrugormerDTI: Drug Graphormer for drug-target interaction prediction

Computers in Biology and Medicine, 2023
Drug-target interactions (DTI) prediction is a crucial task in drug discovery. Existing computational methods accelerate the drug discovery in this respect. However, most of them suffer from low feature representation ability, significantly affecting the
Jiayue Hu, Yu Wang, Chao Pang, Junru Jin, Nhat Truong Pham, Balachandran Manavalan, Leyi Wei   +6 more
semanticscholar   +3 more sources

A survey of drug-target interaction and affinity prediction methods via graph neural networks

Comput. Biol. Medicine, 2023
The tasks of drug-target interaction (DTI) and drug-target affinity (DTA) prediction play important roles in the field of drug discovery. However, biological experiment-based methods are time-consuming and expensive.
Yue Zhang, Yuqing Hu, Na Han, Aqing Yang, Xiaoyong Liu, Hongmin Cai   +5 more
semanticscholar   +1 more source

TripletMultiDTI: Multimodal representation learning in drug-target interaction prediction with triplet loss function

Expert systems with applications, 2023
Background In drug discovery, drug-target interaction (DTI) plays a crucial role. Identifying DTI in a wet-lab experiment is time-consuming, labor-intensive, and costly. Using reliable computational methods to predict DTI mitigates the enormous costs and
Alireza Dehghan, Parvin Razzaghi, Karim Abbasi, Sajjad Gharaghani   +3 more
semanticscholar   +1 more source