Molecular machine learning has been maturing rapidly over the last few years. Improved
methods and the presence of larger datasets have enabled machine learning algorithms to …

Deep learning has achieved remarkable success in learning representations for molecules,
which is crucial for various biochemical applications, ranging from property prediction to …

Standfirst Over the last decade, we have witnessed the emergence of ever more machine
learning applications in all aspects of the chemical sciences. Here, we highlight specific …

GNNs and chemical fingerprints are the predominant approaches to representing molecules
for property prediction. However, in NLP, transformers have become the de-facto standard …

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

With the recent advancement of deep learning, molecular representation learning--
automating the discovery of feature representation of molecular structure, has attracted …

Abstract Molecular Property Prediction (MPP) is a crucial task in the AI-driven Drug
Discovery (AIDD) pipeline, which has recently gained considerable attention thanks to …

Molecule representation learning (MRL) methods aim to embed molecules into a real vector
space. However, existing SMILES-based (Simplified Molecular-Input Line-Entry System) or …

Research in chemistry increasingly requires interdisciplinary work prompted by, among
other things, advances in computing, machine learning, and artificial intelligence. Everyone …

There has been a recent surge of interest in using machine learning across chemical space
in order to predict properties of molecules or design molecules and materials with the …