Recently, using artificial intelligence (AI) in drug discovery has received much attention
since it significantly shortens the time and cost of develo** new drugs. Deep learning (DL) …

With the explosive growth of online information, recommender systems play a key role to
alleviate such information overload. Due to the important application value of recommender …

Graph neural networks (GNNs) have been widely used in molecular property prediction, but
explaining their black-box predictions is still a challenge. Most existing explanation methods …

Over the last few years, the Shapley value, a solution concept from cooperative game theory,
has found numerous applications in machine learning. In this paper, we first discuss …

Interpretable graph learning is in need as many scientific applications depend on learning
models to collect insights from graph-structured data. Previous works mostly focused on …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

As explanations are increasingly used to understand the behavior of graph neural networks
(GNNs), evaluating the quality and reliability of GNN explanations is crucial. However …

Deep learning methods are achieving ever-increasing performance on many artificial
intelligence tasks. A major limitation of deep models is that they are not amenable to …

Intrinsic interpretability of graph neural networks (GNNs) is to find a small subset of the input
graph's features--rationale--which guides the model prediction. Unfortunately, the leading …

Learning effective molecular feature representation to facilitate molecular property prediction
is of great significance for drug discovery. Recently, there has been a surge of interest in pre …