Abstract Structure-based drug design uses three-dimensional geometric information of
macromolecules, such as proteins or nucleic acids, to identify suitable ligands. Geometric …

Molecular docking plays a significant role in early-stage drug discovery, from structure-
based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive …

Predicting how a drug-like molecule binds to a specific protein target is a core problem in
drug discovery. An extremely fast computational binding method would enable key …

Protein complex formation is a central problem in biology, being involved in most of the cell's
processes, and essential for applications, eg drug design or protein engineering. We tackle …

Unlike vision and language data which usually has a unique format, molecules can naturally
be characterized using different chemical formulations. One can view a molecule as a 2D …

Recently many efforts have been devoted to applying graph neural networks (GNNs) to
molecular property prediction which is a fundamental task for computational drug and …

Predicting protein–ligand binding affinities (PLAs) is a core problem in drug discovery.
Recent advances have shown great potential in applying machine learning (ML) for PLA …

Protein engineering is an emerging field in biotechnology that has the potential to
revolutionize various areas, such as antibody design, drug discovery, food security, ecology …

Graph neural networks have emerged as a leading architecture for many graph-level tasks,
such as graph classification and graph generation. As an essential component of the …

AI-aided drug discovery (AIDD) is gaining increasing popularity due to its promise of making
the search for new pharmaceuticals quicker, cheaper and more efficient. In spite of its …