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 …

Abstract Machine learning plays an increasingly important role in many areas of chemistry
and materials science, being used to predict materials properties, accelerate simulations …

Despite advances in molecular biology, genetics, computation, and medicinal chemistry,
infectious disease remains an ominous threat to public health. Addressing the challenges …

Drug designing and development is an important area of research for pharmaceutical
companies and chemical scientists. However, low efficacy, off-target delivery, time …

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 …

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

Subgraph GNNs are a recent class of expressive Graph Neural Networks (GNNs) which
model graphs as collections of subgraphs. So far, the design space of possible Subgraph …

Large language models (LLMs), such as GPT4 and LLaMA, are creating significant
advancements in natural language processing, due to their strong text encoding/decoding …

Human can leverage prior experience and learn novel tasks from a handful of
demonstrations. In contrast to offline meta-reinforcement learning, which aims to achieve …

Deep learning bears promise for drug discovery, including advanced image analysis,
prediction of molecular structure and function, and automated generation of innovative …