Deep Learning (DL) has recently enabled unprecedented advances in one of the grand
challenges in computational biology: the half-century-old problem of protein structure …

Drug discovery is the process of new drug identification. This process is driven by the
increasing data from existing chemical libraries and data banks. The knowledge graph is …

In the short few months since the release of ChatGPT 1, 2, the potential for large language
models (LLMs) and generative artificial intelligence (AI) to disrupt fields as diverse as art …

Motivation Graph embedding learning that aims to automatically learn low-dimensional
node representations, has drawn increasing attention in recent years. To date, most recent …

Ontologies have long been employed in the life sciences to formally represent and reason
over domain knowledge and they are employed in almost every major biological database …

Large Language Models (LLMs) have taken Knowledge Representation--and the world--by
storm. This inflection point marks a shift from explicit knowledge representation to a renewed …

Motivation A large number of protein sequences are becoming available through the
application of novel high-throughput sequencing technologies. Experimental functional …

Biomedical knowledge graphs (KGs), which can help with the understanding of complex
biological systems and pathologies, have begun to play a critical role in medical practice …

The physical sciences community is increasingly taking advantage of the possibilities
offered by modern data science to solve problems in experimental chemistry and potentially …

Drug-target interaction (DTI) prediction plays a crucial role in drug repositioning and virtual
drug screening. Most DTI prediction methods cast the problem as a binary classification task …