[HTML][HTML] Advances and trends in omics technology development
X Dai, L Shen - Frontiers in Medicine, 2022 - frontiersin.org
The human history has witnessed the rapid development of technologies such as high-
throughput sequencing and mass spectrometry that led to the concept of 'omics' and …
throughput sequencing and mass spectrometry that led to the concept of 'omics' and …
Toward better drug discovery with knowledge graph
Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …
increasing data from existing chemical libraries and data banks. The knowledge graph is …
VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares
L Shen, F Liu, L Huang, G Liu, L Zhou… - Computers in biology and …, 2022 - Elsevier
Background A new coronavirus disease named COVID-19, caused by severe acute
respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide …
respiratory syndrome coronavirus-2 (SARS-CoV-2), is rapidly spreading worldwide …
MGraphDTA: deep multiscale graph neural network for explainable drug–target binding affinity prediction
Predicting drug–target affinity (DTA) is beneficial for accelerating drug discovery. Graph
neural networks (GNNs) have been widely used in DTA prediction. However, existing …
neural networks (GNNs) have been widely used in DTA prediction. However, existing …
ML-DTI: mutual learning mechanism for interpretable drug–target interaction prediction
Deep learning (DL) provides opportunities for the identification of drug–target interactions
(DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most …
(DTIs). The challenges of applying DL lie primarily with the lack of interpretability. Also, most …
PreDTIs: prediction of drug–target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection …
SMH Mahmud, W Chen, Y Liu, MA Awal… - Briefings in …, 2021 - academic.oup.com
Discovering drug–target (protein) interactions (DTIs) is of great significance for researching
and developing novel drugs, having a tremendous advantage to pharmaceutical industries …
and developing novel drugs, having a tremendous advantage to pharmaceutical industries …
Metapath-aggregated heterogeneous graph neural network for drug–target interaction prediction
Drug–target interaction (DTI) prediction is an essential step in drug repositioning. A few
graph neural network (GNN)-based methods have been proposed for DTI prediction using …
graph neural network (GNN)-based methods have been proposed for DTI prediction using …
TransDTI: transformer-based language models for estimating DTIs and building a drug recommendation workflow
The identification of novel drug–target interactions is a labor-intensive and low-throughput
process. In silico alternatives have proved to be of immense importance in assisting the drug …
process. In silico alternatives have proved to be of immense importance in assisting the drug …
Water network-augmented two-state model for protein–ligand binding affinity prediction
X Qu, L Dong, D Luo, Y Si, B Wang - Journal of Chemical …, 2023 - ACS Publications
Water network rearrangement from the ligand-unbound state to the ligand-bound state is
known to have significant effects on the protein–ligand binding interactions, but most of the …
known to have significant effects on the protein–ligand binding interactions, but most of the …
Multitask joint strategies of self-supervised representation learning on biomedical networks for drug discovery
Self-supervised representation learning (SSL) on biomedical networks provides new
opportunities for drug discovery; however, effectively combining multiple SSL models is still …
opportunities for drug discovery; however, effectively combining multiple SSL models is still …