Multiphysical graph neural network (MP-GNN) for COVID-19 drug design.

Graph neural networks (GNNs) are the most promising deep learning models that can revolutionize non-Euclidean data analysis. However, their full potential is severely curtailed by poorly represented molecular graphs and features. Here, we propose a multiphysical graph neural network (MP-GNN) model based on the developed multiphysical molecular graph representation and featurization. All kinds of molecular interactions, between different atom types and at different scales, are systematically represented by a series of scale-specific and element-specific graphs with distance-related node features. From these graphs, graph convolution network (GCN) models are constructed with specially designed weight-sharing architectures. Base learners are constructed from GCN models from different elements at different scales, and further consolidated together using both one-scale and multi-scale ensemble learning schemes. Our MP-GNN has two distinct properties. First, our MP-GNN incorporates multiscale interactions using more than one molecular graph. Atomic interactions from various different scales are not modeled by one specific graph (as in traditional GNNs), instead they are represented by a series of graphs at different scales. Second, it is free from the complicated feature generation process as in conventional GNN methods. In our MP-GNN, various atom interactions are embedded into element-specific graph representations with only distance-related node features. A unique GNN architecture is designed to incorporate all the information into a consolidated model. Our MP-GNN has been extensively validated on the widely used benchmark test datasets from PDBbind, including PDBbind-v2007, PDBbind-v2013 and PDBbind-v2016. Our model can outperform all existing models as far as we know. Further, our MP-GNN is used in coronavirus disease 2019 drug design. Based on a dataset with 185 complexes of inhibitors for severe acute respiratory syndrome coronavirus (SARS-CoV/SARS-CoV-2), we evaluate their binding affinities using our MP-GNN. It has been found that our MP-GNN is of high accuracy. This demonstrates the great potential of our MP-GNN for the screening of potential drugs for SARS-CoV-2. Availability: The Multiphysical graph neural network (MP-GNN) model can be found in https://github.com/Alibaba-DAMO-DrugAI/MGNN. Additional data or code will be available upon reasonable request.

Detection of SARS-CoV-2 in saliva and characterization of oral symptoms in COVID-19 patients.

OBJECTIVES: In order to provide a more comprehensive understanding of the effects of SARS-CoV-2 on oral health and possible saliva transmission, we performed RNA-seq profiles analysis from public databases and also a questionnaire survey on oral-related symptoms of COVID-19 patients. MATERIALS AND METHODS: To analyse ACE2 expression in salivary glands, bulk RNA-seq profiles from four public datasets including 31 COVID-19 patients were recruited. Saliva and oropharyngeal swabs were collected. SARS-CoV-2 nucleic acids in saliva were detected by real-time polymerase chain reaction (RT-PCR). Additionally, a questionnaire survey on various oral symptoms such as dry mouth and amblygeustia was also carried out on COVID-19 patients. RESULTS: ACE2 expression was present at detectable levels in the salivary glands. In addition, of four cases with positive detection of salivary SARS-CoV-2 nucleic acids, three (75%) were critically ill on ventilator support. Furthermore, we observed the two major oral-related symptoms, dry mouth (46.3%) and amblygeustia (47.2%), were manifested by a relatively high proportion of 108 COVID-19 patients who accepted the questionnaire survey. CONCLUSIONS: This study confirms the expression of ACE2 in the salivary glands and demonstrates the possibility of SARS-CoV-2 infection of salivary glands. Saliva may be a new source of diagnostic specimens for critically ill patients, since it can be easily collected without any invasive procedures. In addition, dry mouth and amblygeustia can be considered as initial symptoms of COVID-19 infection.