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

The rapid and accurate in silico prediction of protein-ligand binding free energies or binding
affinities has the potential to transform drug discovery. In recent years, several deep learning …

Structure-based drug design (SBDD), which utilizes the three-dimensional geometry of
proteins to identify potential drug candidates, is becoming increasingly vital in drug …

The core of large-scale drug virtual screening is to select the binders accurately and
efficiently with high affinity from large libraries of small molecules in which non-binders are …

Protein–ligand binding affinity (PLBA) prediction is the fundamental task in drug discovery.
Recently, various deep learning-based models predict binding affinity by incorporating the …

Graph neural networks (GNNs), which are capable of learning representations from
graphical data, are naturally suitable for modeling molecular systems. This review …

Abstract 3D structure-based drug design (SBDD) is considered a challenging and rational
way for innovative drug discovery. Geometric deep learning is a promising approach that …

We propose HydraScreen, a deep-learning approach that aims to provide a framework for
more robust machine-learning-accelerated drug discovery. HydraScreen utilizes a state-of …

Introduction Modern drug discovery revolves around designing ligands that target the
chosen biomolecule, typically proteins. For this, the evaluation of affinities of putative ligands …

Developing new drugs is too expensive and time-consuming. Accurately predicting the
interaction between drugs and targets will likely change how the drug is discovered …