E-SAGE: Explainability-based Defense Against Backdoor Attacks on Graph Neural Networks

Graph Neural Networks (GNNs) have recently been widely adopted in multiple domains. Yet, they are notably vulnerable to adversarial and backdoor attacks. In particular, backdoor attacks based on subgraph insertion have been shown to be effective in graph classification tasks while being stealthy, successfully circumventing various existing defense methods. In this paper, we propose E-SAGE, a novel approach to defending GNN backdoor attacks based on explainability. We find that the malicious edges and benign edges have significant differences in the importance scores for explainability evaluation. Accordingly, E-SAGE adaptively applies an iterative edge pruning process on the graph based on the edge scores. Through extensive experiments, we demonstrate the effectiveness of E-SAGE against state-of-the-art graph backdoor attacks in different attack settings. In addition, we investigate the effectiveness of E-SAGE against adversarial attacks

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

The impact of active social media use on the mental health of older adults

Abstract Background With the rising population age and the development of information technology in China, a growing number of older adults use social media as a means of social participation. The impact of posting on WeChat Moments on the mental health of older adults is worthy of attention. Aim The aim of this study is to identify posting on WeChat Moments as an active social media use and analyze its impact on the mental health of older adults. Method Using the survey data from the China Health and Retirement Longitudinal Study (CHARLS), we defined posting on WeChat Moments as an active social media use and used propensity score matching (PSM) to analyze the impact of such posting on the mental health of older adults. Results The results of the study showed that posting statistically significantly improved the depression, self-rated health, and health satisfaction of older adults. Heterogeneity analysis showed that the female older-adult population and the younger older-adult population derived the most mental health benefit from posting on WeChat Moments. Conclusion Posting on WeChat Moments statistically significantly improved the depression, self-rated health, and health satisfaction of older adults. Older adults who use WeChat and post on WeChat Moments derive much benefit from their active social media use