Towards De Novo Drug Design for the Coronavirus: A Drug-Target Interaction Prediction Approach using Atom-enhanced Graph Neural Network with Multi-hop Gating Mechanism

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.

Extending UTAUT2 to Mobile Money Adoption and Consumer Behavior: An Empirical Study in Vietnam

Using an extended unified theory of acceptance and use of technology, the goal of this paper is to investigate the antecedents of behavioral intention towards mobile money, as well as the mediating effect of trust between behavioral intention and financial inclusion in Vietnam during the COVID-19 period (UTAUT2). The data for this study was obtained via an online self-administered questionnaire, which was then analyzed using the SmartPLS 3.3.3 program. For the purpose of determining the relevance and performance of the exogenous constructs, an importance-performance matrix analysis was performed. The findings of this study suggest that knowledge, structural assurance, habit, and performance expectancy are the most important factors influencing users' behavioral intention to use mobile money. In the case of mobile money adoption, the behavioral intention has a significant influence, and trust does not mediate between behavioral intention and financial inclusion. For the first time in Vietnam, the extended UTAUT2 is being used to investigate mobile money usage and adoption patterns. The current study, however, focuses on users' financial inclusion goals rather than their intended behavior.