Established molecular machine learning models process individual molecules as inputs to
predict their biological, chemical, or physical properties. However, such algorithms require …

Shape-based virtual screening is widely employed in ligand-based drug design to search
chemical libraries for molecules with similar 3D shapes yet novel 2D chemical structures …

Here we address the problem of the assignment of atom types and bond orders in low
molecular weight compounds. For this purpose, we have developed a prediction model …

As the size of accessible compound libraries expands to over 10 billion, the need for more
efficient structure-based virtual screening methods is emerging. Different pre-screening …

In drug discovery, it is vital to confirm the predictions of pharmaceutical properties from
computational models using costly wet-lab experiments. Hence, obtaining reliable …

Deep learning has demonstrated significant potential in advancing state of the art in many
problem domains, especially those benefiting from automated feature extraction. Yet, the …

Molecular docking computationally predicts the conformation of a small molecule when
binding to a receptor. Scoring functions are a vital piece of any molecular docking pipeline …

Artificial intelligence (AI) in the form of deep learning bears promise for drug discovery and
chemical biology, $\textit {eg} $, to predict protein structure and molecular bioactivity, plan …

Understanding the interactions formed between a ligand and its molecular target is key to
guiding the optimization of molecules. Different experimental and computational methods …

The necessity to generate conformations that sample the entire conformational space
accessible to a given molecule is ubiquitous in the field of computer-aided drug design …