Only around 20% of the human proteome is considered to be druggable with small-molecule
antagonists. This leaves some of the most compelling therapeutic targets outside the reach …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks and graph transformer architectures, stands out for its capability to capture …

Biomedical data are amassed at an ever-increasing rate, and machine learning tools that
use prior knowledge in combination with biomedical big data are gaining much traction 1, 2 …

In clinical artificial intelligence (AI), graph representation learning, mainly through graph
neural networks (GNNs), stands out for its capability to capture intricate relationships within …

Background Understanding the impact of gene interactions on disease phenotypes is
increasingly recognised as a crucial aspect of genetic disease research. This trend is …

Druggable pockets are protein regions that have the ability to bind organic small molecules,
and their characterization is essential in target-based drug discovery. However, deriving …

Chemical modulation of proteins enables a mechanistic understanding of biology and
represents the foundation of most therapeutics. However, despite decades of research, 80 …

Abstract The European Molecular Biology Laboratory's European Bioinformatics Institute
(EMBL-EBI) is one of the world's leading sources of public biomolecular data. Based at the …

Openly available, collaboratively edited Knowledge Graphs (KGs) are key platforms for the
collective management of evolving knowledge. The present work aims to provide an …

Conventional Artificial Intelligence models are heavily limited in handling complex
biomedical structures (such as 2D or 3D protein and molecule structures) and providing …