Highlights•The deep learning method could effectively represent the molecular structure and
predict molecular property through diversified models.•One, two, and three-dimensional …

In the last decade, deep learning has reinvigorated the machine learning field. It has solved
many problems in computer vision, speech recognition, natural language processing, and …

Molecular machine learning bears promise for efficient molecular property prediction and
drug discovery. However, labelled molecule data can be expensive and time consuming to …

Predicting molecular properties with data-driven methods has drawn much attention in
recent years. Particularly, Graph Neural Networks (GNNs) have demonstrated remarkable …

The clinical efficacy and safety of a drug is determined by its molecular properties and
targets in humans. However, proteome-wide evaluation of all compounds in humans, or …

Abstract Models based on machine learning can enable accurate and fast molecular
property predictions, which is of interest in drug discovery and material design. Various …

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 …

The prediction of quantum mechanical properties is historically plagued by a trade-off
between accuracy and speed. Machine learning potentials have previously shown great …

Many important tasks in chemistry revolve around molecules during reactions. This requires
predictions far from the equilibrium, while most recent work in machine learning for …

We consider representation learning of 3D molecular graphs in which each atom is
associated with a spatial position in 3D. This is an under-explored area of research, and a …