Artificial intelligence (AI) has been transforming the practice of drug discovery in the past
decade. Various AI techniques have been used in many drug discovery applications, such …

Deep learning has been a prevalence in computational chemistry and widely implemented
in molecular property predictions. Recently, self-supervised learning (SSL), especially …

There is increasing adoption of artificial intelligence in drug discovery. However, existing
studies use machine learning to mainly utilize the chemical structures of molecules but …

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 …

Molecular graph representation learning is a fundamental problem in modern drug and
material discovery. Molecular graphs are typically modeled by their 2D topological …

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

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

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 …

A Gentle Introduction to Graph Neural Networks Distill About Prize Submit A Gentle Introduction
to Graph Neural Networks Neural networks have been adapted to leverage the structure and …