1,790 research outputs found
GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination
Recent progress in deep learning is revolutionizing the healthcare domain
including providing solutions to medication recommendations, especially
recommending medication combination for patients with complex health
conditions. Existing approaches either do not customize based on patient health
history, or ignore existing knowledge on drug-drug interactions (DDI) that
might lead to adverse outcomes. To fill this gap, we propose the Graph
Augmented Memory Networks (GAMENet), which integrates the drug-drug
interactions knowledge graph by a memory module implemented as a graph
convolutional networks, and models longitudinal patient records as the query.
It is trained end-to-end to provide safe and personalized recommendation of
medication combination. We demonstrate the effectiveness and safety of GAMENet
by comparing with several state-of-the-art methods on real EHR data. GAMENet
outperformed all baselines in all effectiveness measures, and also achieved
3.60% DDI rate reduction from existing EHR data.Comment: AAAI 2019; change the template and fix some typo
Knowledge-augmented Graph Machine Learning for Drug Discovery: A Survey from Precision to Interpretability
The integration of Artificial Intelligence (AI) into the field of drug
discovery has been a growing area of interdisciplinary scientific research.
However, conventional AI models are heavily limited in handling complex
biomedical structures (such as 2D or 3D protein and molecule structures) and
providing interpretations for outputs, which hinders their practical
application. As of late, Graph Machine Learning (GML) has gained considerable
attention for its exceptional ability to model graph-structured biomedical data
and investigate their properties and functional relationships. Despite
extensive efforts, GML methods still suffer from several deficiencies, such as
the limited ability to handle supervision sparsity and provide interpretability
in learning and inference processes, and their ineffectiveness in utilising
relevant domain knowledge. In response, recent studies have proposed
integrating external biomedical knowledge into the GML pipeline to realise more
precise and interpretable drug discovery with limited training instances.
However, a systematic definition for this burgeoning research direction is yet
to be established. This survey presents a comprehensive overview of
long-standing drug discovery principles, provides the foundational concepts and
cutting-edge techniques for graph-structured data and knowledge databases, and
formally summarises Knowledge-augmented Graph Machine Learning (KaGML) for drug
discovery. A thorough review of related KaGML works, collected following a
carefully designed search methodology, are organised into four categories
following a novel-defined taxonomy. To facilitate research in this promptly
emerging field, we also share collected practical resources that are valuable
for intelligent drug discovery and provide an in-depth discussion of the
potential avenues for future advancements
Learning Multimodal Graph-to-Graph Translation for Molecular Optimization
We view molecular optimization as a graph-to-graph translation problem. The
goal is to learn to map from one molecular graph to another with better
properties based on an available corpus of paired molecules. Since molecules
can be optimized in different ways, there are multiple viable translations for
each input graph. A key challenge is therefore to model diverse translation
outputs. Our primary contributions include a junction tree encoder-decoder for
learning diverse graph translations along with a novel adversarial training
method for aligning distributions of molecules. Diverse output distributions in
our model are explicitly realized by low-dimensional latent vectors that
modulate the translation process. We evaluate our model on multiple molecular
optimization tasks and show that our model outperforms previous
state-of-the-art baselines
- …