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

The first step in the construction of a regression model or a data-driven analysis, aiming to
predict or elucidate the relationship between the atomic-scale structure of matter and its …

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 …

Many real-world data can be modeled as 3D graphs, but learning representations that
incorporates 3D information completely and efficiently is challenging. Existing methods …

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 …

Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key
role in areas of cheminformatics and drug discovery. Existing generative models have …

Abstract Machine learning (ML) outperforms traditional approaches in many molecular
design tasks. ML models usually predict molecular properties from a 2D chemical graph or a …

Massive scale, in terms of both data availability and computation, enables important
breakthroughs in key application areas of deep learning such as natural language …

Conspectus Designing new materials is vital for addressing pressing societal challenges in
health, energy, and sustainability. The combination of physicochemical laws and empirical …