Graph machine learning (GML) is receiving growing interest within the pharmaceutical and
biotechnology industries for its ability to model biomolecular structures, the functional …

Learning effective protein representations is critical in a variety of tasks in biology such as
predicting protein function or structure. Existing approaches usually pretrain protein language …

Structure-based drug design (SBDD) aims to design small-molecule ligands that bind with
high affinity and specificity to pre-determined protein targets. In this paper, we formulate …

Nervous systems contain sensory neurons, local neurons, projection neurons, and motor
neurons. To understand how these building blocks form whole circuits, we must distil these …

Here, we present the use of ethoscopes, which are machines for high-throughput analysis
of behavior in Drosophila and other animals. Ethoscopes provide a software and hardware …

10.7554/eLife.44590.001 Most sensory systems are organized into parallel neuronal pathways
that process distinct aspects of incoming stimuli. In the insect olfactory system, second …

We introduce GAUCHE, an open-source library for GAUssian processes in CHEmistry.
Gaussian processes have long been a cornerstone of probabilistic machine learning, affording …

Geometric deep learning has well-motivated applications in the context of biology, a domain
where relational structure in datasets can be meaningfully leveraged. Currently, efforts in …

Structure-based drug design involves finding ligand molecules that exhibit structural and
chemical complementarity to protein pockets. Deep generative methods have shown promise …

Protein structure representation learning is the foundation for promising applications in drug
discovery, protein design, and protein function prediction. However, there remains a need …