A survey of drug-target interaction and affinity prediction methods via graph neural networks
Y Zhang, Y Hu, N Han, A Yang, X Liu, H Cai - Computers in Biology and …, 2023 - Elsevier
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 …
important roles in the field of drug discovery. However, biological experiment-based …
Machine Learning for Sequence and Structure-Based Protein–Ligand Interaction Prediction
Y Zhang, S Li, K Meng, S Sun - Journal of Chemical Information …, 2024 - ACS Publications
Developing new drugs is too expensive and time-consuming. Accurately predicting the
interaction between drugs and targets will likely change how the drug is discovered …
interaction between drugs and targets will likely change how the drug is discovered …
iPiDA-GCN: Identification of piRNA-disease associations based on Graph Convolutional Network
J Hou, H Wei, B Liu - PLOS Computational Biology, 2022 - journals.plos.org
Motivation Piwi-interacting RNAs (piRNAs) play a critical role in the progression of various
diseases. Accurately identifying the associations between piRNAs and diseases is important …
diseases. Accurately identifying the associations between piRNAs and diseases is important …
A geometric deep learning framework for drug repositioning over heterogeneous information networks
Drug repositioning (DR) is a promising strategy to discover new indicators of approved
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
drugs with artificial intelligence techniques, thus improving traditional drug discovery and …
SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features
S Pan, L Xia, L Xu, Z Li - BMC bioinformatics, 2023 - Springer
Background Drug–target affinity (DTA) prediction is a critical step in the field of drug
discovery. In recent years, deep learning-based methods have emerged for DTA prediction …
discovery. In recent years, deep learning-based methods have emerged for DTA prediction …
Leveraging pre-trained language models for mining microbiome-disease relationships
N Karkera, S Acharya, SK Palaniappan - BMC bioinformatics, 2023 - Springer
Background The growing recognition of the microbiome's impact on human health and well-
being has prompted extensive research into discovering the links between microbiome …
being has prompted extensive research into discovering the links between microbiome …
[HTML][HTML] A brief review of protein–ligand interaction prediction
The task of identifying protein–ligand interactions (PLIs) plays a prominent role in the field of
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …
drug discovery. However, it is infeasible to identify potential PLIs via costly and laborious in …
Advancing drug–target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining
Background The pharmaceutical field faces a significant challenge in validating drug target
interactions (DTIs) due to the time and cost involved, leading to only a fraction being …
interactions (DTIs) due to the time and cost involved, leading to only a fraction being …
MCL-DTI: using drug multimodal information and bi-directional cross-attention learning method for predicting drug–target interaction
Y Qian, X Li, J Wu, Q Zhang - BMC bioinformatics, 2023 - Springer
Background Prediction of drug–target interaction (DTI) is an essential step for drug discovery
and drug reposition. Traditional methods are mostly time-consuming and labor-intensive …
and drug reposition. Traditional methods are mostly time-consuming and labor-intensive …
Drug repurposing and prediction of multiple interaction types via graph embedding
E Amiri Souri, A Chenoweth, SN Karagiannis… - BMC …, 2023 - Springer
Background Finding drugs that can interact with a specific target to induce a desired
therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore …
therapeutic outcome is key deliverable in drug discovery for targeted treatment. Therefore …