Conspectus In structure-based drug design, scoring functions are widely used for fast
evaluation of protein–ligand interactions. They are often applied in combination with …

The arc of drug discovery entails a multiparameter optimization problem spanning vast
length scales. The key parameters range from solubility (angstroms) to protein–ligand …

Docking is now routine in virtual screening or lead optimization for drug screening and
design. The number of papers related to docking has dramatically increased over the past …

In this work, we propose a deep learning approach to improve docking-based virtual
screening. The deep neural network that is introduced, DeepVS, uses the output of a …

Classical scoring functions have reached a plateau in their performance in virtual screening
and binding affinity prediction. Recently, machine-learning scoring functions trained on …

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, there has been a …

Identifying novel drug–protein interactions is crucial for drug discovery. For this purpose,
many machine learning-based methods have been developed based on drug descriptors …

In this study, we introduce an interpretable graph-based deep learning prediction model,
AttentionSiteDTI, which utilizes protein binding sites along with a self-attention mechanism …

In the present communication, the simultaneous co-immobilization by covalent binding of
lipase A from Candida antarctica (CALA) and lipase B from Candida antarctica (CALB) in …

Proteins participate in various essential processes in vivo via interactions with other
molecules. Identifying the residues participating in these interactions not only provides …