Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …
companies and chemical scientists. However, low efficacy, off-target delivery, time …
[HTML][HTML] Integration strategies of multi-omics data for machine learning analysis
Increased availability of high-throughput technologies has generated an ever-growing
number of omics data that seek to portray many different but complementary biological …
number of omics data that seek to portray many different but complementary biological …
Utilizing graph machine learning within drug discovery and development
Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …
biotechnology industries for its ability to model biomolecular structures, the functional …
Deep learning for drug repurposing: Methods, databases, and applications
Drug development is time‐consuming and expensive. Repurposing existing drugs for new
therapies is an attractive solution that accelerates drug development at reduced …
therapies is an attractive solution that accelerates drug development at reduced …
Graph neural network approaches for drug-target interactions
Developing new drugs remains prohibitively expensive, time-consuming, and often involves
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …
safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug …
Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers
Computational drug repurposing aims to identify new indications for existing drugs by
utilizing high-throughput data, often in the form of biomedical knowledge graphs. However …
utilizing high-throughput data, often in the form of biomedical knowledge graphs. However …
Fusing higher and lower-order biological information for drug repositioning via graph representation learning
Drug repositioning is a promising drug development technique to identify new indications for
existing drugs. However, existing computational models only make use of lower-order …
existing drugs. However, existing computational models only make use of lower-order …
MVGCN: data integration through multi-view graph convolutional network for predicting links in biomedical bipartite networks
Motivation There are various interaction/association bipartite networks in biomolecular
systems. Identifying unobserved links in biomedical bipartite networks helps to understand …
systems. Identifying unobserved links in biomedical bipartite networks helps to understand …
IEA-GNN: Anchor-aware graph neural network fused with information entropy for node classification and link prediction
Graph neural networks are essential in mining complex relationships in graphs. However,
most methods ignore the global location information of nodes and the discrepancy between …
most methods ignore the global location information of nodes and the discrepancy between …
Graph neural networks designed for different graph types: A survey
JM Thomas, A Moallemy-Oureh… - arXiv preprint arXiv …, 2022 - arxiv.org
Graphs are ubiquitous in nature and can therefore serve as models for many practical but
also theoretical problems. For this purpose, they can be defined as many different types …
also theoretical problems. For this purpose, they can be defined as many different types …