Artificial intelligence (AI) is being increasingly integrated into scientific discovery to augment
and accelerate research, helping scientists to generate hypotheses, design experiments …

Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …

Molecular optimization is a fundamental goal in the chemical sciences and is of central
interest to drug and material design. In recent years, significant progress has been made in …

Artificial intelligence for graphs has achieved remarkable success in modelling complex
systems, ranging from dynamic networks in biology to interacting particle systems in physics …

Despite tremendous progress of generative models in the natural sciences, their
controllability remains challenging. One fundamentally missing aspect of molecular or …

Graphs are ubiquitous in encoding relational information of real-world objects in many
domains. Graph generation, whose purpose is to generate new graphs from a distribution …

Molecule design is a fundamental problem in molecular science and has critical applications
in a variety of areas, such as drug discovery, material science, etc. However, due to the large …

Abstract Structure-based drug design (SBDD) aims to discover drug candidates by finding
molecules (ligands) that bind tightly to a disease-related protein (targets), which is the …

The binding complexes formed by proteins and small molecule ligands are ubiquitous and
critical to life. Despite recent advancements in protein structure prediction, existing …

Chemists can be skeptical in using deep learning (DL) in decision making, due to the lack of
interpretability in “black-box” models. Explainable artificial intelligence (XAI) is a branch of …