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 …

Large language models (LLMs) have been shown to have significant potential in few-shot
learning across various fields, even with minimal training data. However, their ability to …

The discovery of new hits through ligand-based virtual screening in drug discovery is
essentially a low-data problem, as data acquisition is both difficult and expensive. The …

We develop a novel approach to conformal prediction when the target task has limited data
available for training. Conformal prediction identifies a small set of promising output …

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 …

Molecular property prediction plays a fundamental role in drug discovery to identify
candidate molecules with target properties. However, molecular property prediction is …

Small datasets are ubiquitous in drug discovery as data generation is expensive and can be
restricted for ethical reasons (eg in vivo experiments). A widely applied technique in early …

Recent advances in machine learning have made significant contributions to drug discovery.
Deep neural networks in particular have been demonstrated to provide significant boosts in …

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 …

In order to quickly adapt to new data, few-shot learning aims at learning from few examples,
often by using already acquired knowledge. The new data often differs from the previously …