How to produce expressive molecular representations is a fundamental challenge in
artificial intelligence-driven drug discovery. Graph neural network (GNN) has emerged as a …

How to obtain informative representations of molecules is a crucial prerequisite in AI-driven
drug design and discovery. Recent researches abstract molecules as graphs and employ …

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 …

In computer-aided drug discovery, quantitative structure activity relation models are trained
to predict biological activity from chemical structure. Despite the recent success of applying …

Hunting for chemicals with favorable pharmacological, toxicological, and pharmacokinetic
properties remains a formidable challenge for drug discovery. Deep learning provides us …

Molecular graph representation learning has shown considerable strength in molecular
analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre …

Molecular representation is a critical part of various prediction tasks for physicochemical
properties of molecules and drug design. As graph notations are common in expressing the …

Predicating macroscopic influences of drugs on human body, like efficacy and toxicity, is a
central problem of small-molecule based drug discovery. Molecules can be represented as …

Molecular graph generation is a fundamental problem for drug discovery and has been
attracting growing attention. The problem is challenging since it requires not only generating …

Much of the recent work on learning molecular representations has been based on Graph
Convolution Networks (GCN). These models rely on local aggregation operations and can …