Predicting accurate protein–ligand binding affinities is an important task in drug discovery
but remains a challenge even with computationally expensive biophysics-based energy …

Predicting the native or near-native binding pose of a small molecule within a protein
binding pocket is an extremely important task in structure-based drug design, especially in …

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 …

Applying deep learning concepts from image detection and graph theory has greatly
advanced protein–ligand binding affinity prediction, a challenge with enormous ramifications …

Following the formalism used for the development of the knowledge-based scoring function
DrugScore, new distance-dependent pair potentials are obtained from nonbonded …

Today high-throughput docking is one of the most commonly used computational tools in
drug lead discovery. While there has been an impressive methodological improvement in …

The screening of compound–protein interactions (CPIs) is one of the most crucial steps in
finding hit and lead compounds. Deep learning (DL) methods for CPI prediction can address …

In silico assessment of protein receptor interactions with small ligands is now part of the
standard pipeline for drug discovery, and numerous tools and protocols have been …

Drug discovery is a rigorous process that requires billion dollars of investments and decades
of research to bring a molecule “from bench to a bedside”. While virtual docking can …

One of the main challenges in drug discovery is predicting protein–ligand binding affinity.
Recently, machine learning approaches have made substantial progress on this task …