Time series are the primary data type used to record dynamic system measurements and
generated in great volume by both physical sensors and online processes (virtual sensors) …

Transitioning from fossil fuels to renewable energy sources is a critical global challenge; it
demands advances—at the materials, devices and systems levels—for the efficient …

Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …

In many ways, graphs are the main modality of data we receive from nature. This is due to
the fact that most of the patterns we see, both in natural and artificial systems, are elegantly …

Molecular conformer generation is a fundamental task in computational chemistry. Several
machine learning approaches have been developed, but none have outperformed state-of …

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

Geometric deep learning (GDL) is based on neural network architectures that incorporate
and process symmetry information. GDL bears promise for molecular modelling applications …

Construction of a scaffold structure that supports a desired motif, conferring protein function,
shows promise for the design of vaccines and enzymes. But a general solution to this motif …

Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …

Molecular property prediction is one of the fastest-growing applications of deep learning with
critical real-world impacts. Although the 3D molecular graph structure is necessary for …