Results 61 to 70 of about 5,508,972 (143)

Dipeptide Frequency of Word Frequency and Graph Convolutional Networks for DTA Prediction

Frontiers in Bioengineering and Biotechnology, 2020
Deep learning is an effective method to capture drug-target binding affinity, but low accuracy is still an obstacle to be overcome. Thus, we propose a novel predictor for drug-target binding affinity based on dipeptide frequency of word frequency ...
Xianfang Wang, Xianfang Wang, Yifeng Liu, Fan Lu, Hongfei Li, Peng Gao, Dongqing Wei   +6 more
doaj   +1 more source

Drug-target affinity prediction method based on consistent expression of heterogeneous data

, 2022
The first step in drug discovery is finding drug molecule moieties with medicinal activity against specific targets. Therefore, it is crucial to investigate the interaction between drug-target proteins and small chemical molecules.
Liu, Boyuan
core  

ResDTA: Predicting Drug-Target Binding Affinity Using Residual Skip Connections

, 2023
The discovery of novel drug target (DT) interactions is an important step in the drug development process. The majority of computer techniques for predicting DT interactions have focused on binary classification, with the goal of determining whether or ...
Ghosh, Partho, Haque, Md. Aynal
core  

ViDTA: Enhanced Drug-Target Affinity Prediction via Virtual Graph Nodes and Attention-based Feature Fusion [PDF]

IEEE International Conference on Bioinformatics and Biomedicine
Drug-target interaction is fundamental in understanding how drugs affect biological systems, and accurately predicting drug-target affinity (DTA) is vital for drug discovery.
Minghui Li, Zikang Guo, Yang Wu, Pei-Jin Guo, Yao Shi, Sheng-Shan Hu, Wei Wan, Sheng-Qing Hu   +7 more
semanticscholar   +1 more source

Bispecific antibodies: A guide to model informed drug discovery and development

Heliyon, 2021
Affinity (KD) optimization of monoclonal antibodies is one of the factors that impacts the stoichiometric binding and the corresponding efficacy of a drug.
Irina Kareva, Anup Zutshi, Pawan Gupta, Senthil Kabilan   +3 more
doaj   +1 more source

Drug-target-PPI associations for HMI.

, 2022
(A) Relationship between anti-HMI drug candidates and HMI-associated PPIs. Top 10 PPIs with the highest number of shortest links to drug targets are shown (one PPI may correspond to multiple targets). (B) Closest PPIs regulated by raloxifene. (C) Closest
Yao Jiang (1469614), Jia-Xuan Zhang (14279440), Rong Liu (202610)   +2 more
core   +1 more source

Drug-target-PPI associations for MHCI.

, 2022
(A) Relationship between anti-MHCI drug candidates and MHCI-associated PPIs. Top 10 PPIs with the highest number of shortest links to drug targets are shown (one PPI may correspond to multiple targets).
Yao Jiang (1469614), Jia-Xuan Zhang (14279440), Rong Liu (202610)   +2 more
core   +1 more source

Drug-target-PPI associations for MMI.

, 2022
(A) Relationship between anti-MMI drug candidates and MMI-associated PPIs. Top 10 PPIs with the highest number of shortest links to drug targets are shown (one PPI may correspond to multiple targets). (B) Closest PPIs regulated by azithromycin.
Yao Jiang (1469614), Jia-Xuan Zhang (14279440), Rong Liu (202610)   +2 more
core   +1 more source

DCI-SiteDTA: drug-target affinity prediction based on binding sites detection and site-aware dual cross-interaction block

BMC Bioinformatics
Background Predicting the binding affinity between drugs and proteins is crucial for accelerating drug discovery. However, traditional research methods typically treat binding site detection and affinity prediction as two separate tasks, lacking ...
Jinyang Zhang, Xingyang Li, Bo Wei, Yuni Zeng   +3 more
doaj   +1 more source

PocketDTA: an advanced multimodal architecture for enhanced prediction of drug−target affinity from 3D structural data of target binding pockets

Bioinform.
Motivation Accurately predicting the drug−target binding affinity (DTA) is crucial to drug discovery and repurposing. Although deep learning has been widely used in this field, it still faces challenges with insufficient generalization performance ...
Long Zhao, Hongmei Wang, Shaoping Shi
semanticscholar   +1 more source