Molecular Representation Learning (MRL) has proven impactful in numerous biochemical
applications such as drug discovery and enzyme design. While Graph Neural Networks …

Molecular representation learning (MRL) is a key step to build the connection between
machine learning and chemical science. In particular, it encodes molecules as numerical …

Virtual screening can accelerate drug discovery by identifying promising candidates for
experimental evaluation. Machine learning is a powerful method for screening, as it can …

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

Molecular representation learning is crucial for the problem of molecular property prediction,
where graph neural networks (GNNs) serve as an effective solution due to their structure …

Machine learning as a tool for chemical space exploration broadens horizons to work with
known and unknown molecules. At its core lies molecular representation, an essential key to …

Effective molecular representation learning is of great importance to facilitate molecular
property prediction. Recent advances for molecular representation learning have shown …

Molecular representation learning (MRL) is a specialized field in which deep-learning
models condense essential molecular information into a vectorized form. Whereas recent …

Molecular representation learning can preserve meaningful molecular structures as
embedding vectors, which is a necessary prerequisite for molecular property prediction. Yet …

Molecular shape and geometry dictate key biophysical recognition processes, yet many
graph neural networks disregard 3D information for molecular property prediction. Here, we …