The SARS-CoV2 pandemic has highlighted the importance of efficient and effective methods
for identification of therapeutic drugs, and in particular has laid bare the need for methods …

In the presence of structural data, one sometimes need to compare 3D ligands. We design
an overlay-free method to rank order 3D molecules in the pharmacophore feature space …

Pharmacophore models are widely used as efficient virtual screening (VS) filters for the
target-directed enrichment of large compound libraries. However, the generation of …

The identification of active binding drugs for target proteins (referred to as drug–target
interaction prediction) is the key challenge in virtual screening, which plays an essential role …

Many recently proposed structure-based virtual screening models appear to be able to
accurately distinguish high affinity binders from non-binders. However, several recent …

Machine-learning-based scoring functions (MLSFs) have gained attention for their potential
to improve accuracy in binding affinity prediction and structure-based virtual screening …

Accurate and rapid prediction of protein–ligand interactions (PLIs) is the fundamental
challenge of drug discovery. Deep learning methods have been harnessed for this purpose …

Abstract Structure‐based drug design is a widely applied approach in the discovery of new
lead compounds for known therapeutic targets. In most structure‐based drug design …

The accurate prediction of protein–ligand binding affinity is critical for the success of
computer-aided drug discovery. However, the accuracy of current scoring functions is …

The synthetic medicinal chemist plays a vital role in drug discovery. Today there are AI tools
to guide next syntheses, but many are “Black Boxes”(BB). One learns little more than the …