ABSTRACT
For humans, the COVID-19 pandemic and Coronavirus have undeniably been a nightmare. Although there are effective vaccines, specific drugs are still urgent. Normally, to identify potential drugs, one needs to design and then test interactions between the drug and the virus in an in silico manner for determining candidates. This Drug-Target Interaction (DTI) process, can be done by molecular docking, which is too complicated and time-consuming for manual works. Therefore, it opens room for applying Artificial Intelligence (AI) techniques. In particular, Graph Neural Network (GNN) attracts recent attention since its high suitability for the nature of drug compounds and virus proteins. However, to introduce such a representation well-reflecting biological structures of biological compounds is not a trivial task. Moreover, since available datasets of Coronavirus are still not highly popular, the recently developed GNNs have been suffering from overfitting on them. We then address those issues by proposing a novel model known as Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism. On one hand, our model can learn more precise features of compounds and proteins. On the other hand, we introduce a new gating mechanism to create better atom representation from non-neighbor information. Once applying transfer learning from very large databanks, our model enjoys promising performance, especially when experimenting with Coronavirus. © 2022 IEEE.
ABSTRACT
The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe from the beginning of 2020 and people worldwide have been receiving news about the same from government offices, press conferences and various other media outlets. The COVID-19 Information Watcher Project started in 2020 to collect and organize reliable information sources worldwide. However, it is difficult to automatically identify reliable information sources in foreign countries for several reasons. First, what kind of information sources are reliable heavily depend on each county situation. In some countries people trust their government's official information but in other countries they do not. Secondly, such reliable information sources often provide information in their local languages. Reliable information sources are not necessarily top-ranked by search engines. Crowdsourcing is a promising way to deal with such a case. However, crowd-sourcing platforms do not cover crowds in all countries. In this study, we report some results of our attempt to collect local information regarding COVID-19 from several countries through multi-hop crowdsourcing, in which we allow crowd workers on a crowdsourcing platform to use other platforms in other countries. We show two case studies, Russia and Afghanistan. Our results show that the multi-hop crowdsourcing is a promising way to collect COVID-19 information from different countries. © 2021 IEEE.