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

Lithium-ion batteries play a pivotal role in a wide range of applications, from electronic
devices to large-scale electrified transportation systems and grid-scale energy storage …

Molecular representation learning (MRL) has gained tremendous attention due to its critical
role in learning from limited supervised data for applications like drug design. In most MRL …

Deep learning models can accurately predict molecular properties and help making the
search for potential drug candidates faster and more efficient. Many existing methods are …

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 …

Unlike vision and language data which usually has a unique format, molecules can naturally
be characterized using different chemical formulations. One can view a molecule as a 2D …

Activity and property prediction models are the central workhorses in drug discovery and
materials sciences, but currently, they have to be trained or fine-tuned for new tasks. Without …

In this study, we introduce the count-based Morgan fingerprint (C-MF) to represent chemical
structures of contaminants and develop machine learning (ML)-based predictive models for …

This paper studies few-shot molecular property prediction, which is a fundamental problem
in cheminformatics and drug discovery. More recently, graph neural network based model …

Recently many efforts have been devoted to applying graph neural networks (GNNs) to
molecular property prediction which is a fundamental task for computational drug and …