Accurately predicting drug-drug interactions (DDI) for emerging drugs, which
offer possibilities for treating and alleviating diseases, with computational
methods can improve patient care and contribute to efficient drug development.
However, many existing computational methods require large amounts of known DDI
information, which is scarce for emerging drugs. In this paper, we propose
EmerGNN, a graph neural network (GNN) that can effectively predict interactions
for emerging drugs by leveraging the rich information in biomedical networks.
EmerGNN learns pairwise representations of drugs by extracting the paths
between drug pairs, propagating information from one drug to the other, and
incorporating the relevant biomedical concepts on the paths. The different
edges on the biomedical network are weighted to indicate the relevance for the
target DDI prediction. Overall, EmerGNN has higher accuracy than existing
approaches in predicting interactions for emerging drugs and can identify the
most relevant information on the biomedical network.

提出了 EmerGNN，一种利用生物医学网络中丰富信息能够有效预测新兴药物相互作用的图神经网络。EmerGNN 通过提取药物对之间的路径、在药物之间传播信息，并结合路径上的相关生物医学概念来学习药物的成对表示。权重不同的生物医学网络边指示了目标药物相互作用预测的相关性。总体而言，EmerGNN 在预测新兴药物相互作用方面具有比现有方法更高的准确性，并能识别出生物医学网络上最相关的信息。

基于流程图神经网络的生物医学网络引发的新型药物相互作用预测

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural  Network with Biomedical Network

Self-supervised representation learning (SSL) on biomedical networks provides
new opportunities for drug discovery. However, how to effectively combine
multiple SSL models is still challenging and has been rarely explored.
Therefore, we propose multi-task joint strategies of self-supervised
representation learning on biomedical networks for drug discovery, named
MSSL2drug. We design six basic SSL tasks inspired by various modality features
including structures, semantics, and attributes in heterogeneous biomedical
networks. Importantly, fifteen combinations of multiple tasks are evaluated by
a graph attention-based multi-task adversarial learning framework in two drug
discovery scenarios. The results suggest two important findings. (1)
Combinations of multimodal tasks achieve the best performance compared to other
multi-task joint models. (2) The local-global combination models yield higher
performance than random two-task combinations when there are the same size of
modalities. Therefore, we conjecture that the multimodal and local-global
combination strategies can be treated as the guideline of multi-task SSL for
drug discovery.

本文中提出了一种名为 MSSL2drug 的多任务自我监督表征学习的生物医学网络方法，使用基于图注意力机制的对抗学习框架， 评估了包括结构，语义和属性在内的多种模态特征的六种基本任务，其中的多模态、局部 - 全局组合策略可以被看作是多任务自我监督表征学习用于药物研发的指南。

医学网络自监督表示学习在药物开发中的多任务联合策略

Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery

Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date,
most recent graph embedding methods are evaluated on social and information
networks and are not comprehensively studied on biomedical networks under
systematic experiments and analyses. On the other hand, for a variety of
biomedical network analysis tasks, traditional techniques such as matrix
factorization (which can be seen as a type of graph embedding methods) have
shown promising results, and hence there is a need to systematically evaluate
the more recent graph embedding methods (e.g. random walk-based and neural
network-based) in terms of their usability and potential to further the
state-of-the-art.
We select 11 representative graph embedding methods and conduct a systematic
comparison on 3 important biomedical link prediction tasks: drug-disease
association (DDA) prediction, drug-drug interaction (DDI) prediction,
protein-protein interaction (PPI) prediction; and 2 node classification tasks:
medical term semantic type classification, protein function prediction. Our
experimental results demonstrate that the recent graph embedding methods
achieve promising results and deserve more attention in the future biomedical
graph analysis. Compared with three state-of-the-art methods for DDAs, DDIs and
protein function predictions, the recent graph embedding methods achieve
competitive performance without using any biological features and the learned
embeddings can be treated as complementary representations for the biological
features. By summarizing the experimental results, we provide general
guidelines for properly selecting graph embedding methods and setting their
hyper-parameters for different biomedical tasks.

本文选择 11 种代表性的图嵌入方法，对药物 - 疾病关联、药物 - 药物交互、蛋白质 - 蛋白质相互作用、医学术语语义分类和蛋白功能预测等 5 个生物医学任务的 3 种预测方法进行评估比较，并提出了挑选嵌入方法和设置超参数的普适指导原则。结果表明，最近的图嵌入方法在未使用任何生物学特征的情况下，能够取得与具有生物学特征的方法相媲美的预测性能。