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 …

Scoring functions are important components in molecular docking for structure-based drug
discovery. Traditional scoring functions, generally empirical-or force field-based, are robust …

Structure-based virtual screening (VS) is a widely used approach that employs the
knowledge of the three-dimensional structure of the target of interest in the design of new …

This two‐part Review examines how automation has contributed to different aspects of
discovery in the chemical sciences. In this second part, we reflect on a selection of …

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

Proteins are fundamental biological entities mediating key roles in cellular function and
disease. This paper introduces a multi-scale graph construction of a protein–HoloProt …

Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy
highly depends on the reliability of scoring functions (SFs). With the rapid development of …

Reports of successful applications of machine learning (ML) methods in structure-based
virtual screening (SBVS) are increasing. ML methods such as convolutional neural networks …

Generating molecules that bind to specific proteins is an important but challenging task in
drug discovery. Previous works usually generate atoms in an auto-regressive way, where …

Rational compound design remains a challenging problem for both computational methods
and medicinal chemists. Computational generative methods have begun to show promising …