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

Self-driving laboratories (SDLs) promise an accelerated application of the scientific method.
Through the automation of experimental workflows, along with autonomous experimental …

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 …

Whether current or near-term AI systems could be conscious is a topic of scientific interest
and increasing public concern. This report argues for, and exemplifies, a rigorous and …

Combinatorial optimization (CO) problems are often NP-hard and thus out of reach for exact
algorithms, making them a tempting domain to apply machine learning methods. The highly …

Generating molecules that bind to specific proteins is an important but challenging task in
drug discovery. Previous works usually generate atoms in an auto-regressive way, where …

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 …

Generative flow networks (GFlowNets) are a family of algorithms that learn a generative
policy to sample discrete objects $ x $ with non-negative reward $ R (x) $. Learning …

Latent variable models (LVMs) with discrete compositional latents are an important but
challenging setting due to a combinatorially large number of possible configurations of the …

Molecular representation pretraining is critical in various applications for drug and material
discovery due to the limited number of labeled molecules, and most existing work focuses …