Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …

Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

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

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 …

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 …

Generating molecules with high binding affinities to target proteins (aka structure-based
drug design) is a fundamental and challenging task in drug discovery. Recently, deep …

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 …

A central task in computational drug discovery is to construct models from known active
molecules to find further promising molecules for subsequent screening. However, typically …

Graph representation learning has attracted tremendous attention due to its remarkable
performance in many real-world applications. However, prevailing supervised graph …

We propose Adaptive Deep Kernel Fitting with Implicit Function Theorem (ADKF-IFT), a
novel framework for learning deep kernel Gaussian processes (GPs) by interpolating …