The worldwide increase and proliferation of drug resistant microbes, coupled with the lag in
new drug development, represents a major threat to human health. In order to reduce the …

A pervasive challenge in drug design is determining how to expand a ligand─ a small
molecule that binds to a target biomolecule─ in order to improve various properties of the …

Ensemble docking can be a successful virtual screening technique that addresses the
innate conformational heterogeneity of macromolecular drug targets. Yet, lacking a method …

Over the past few years, many machine learning-based scoring functions for predicting the
binding of small molecules to proteins have been developed. Their objective is to …

Ligand-based virtual screening (LBVS) can be pivotal for identifying potential drug leads,
especially when the target protein's structure is unknown. However, current LBVS methods …

The goal of structure-based drug discovery is to find small molecules that bind to a given
target protein. Deep learning has been used to generate drug-like molecules with certain …

The accurate prediction of molecular conformations with high efficiency is crucial in various
fields such as materials science, computational chemistry and computer‐aided drug design …

The last few years have seen the development of numerous deep learning-based protein–
ligand docking methods. They offer huge promise in terms of speed and accuracy. However …

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 …

Structure-based virtual screening has been a crucial tool in drug discovery for decades.
However, as the chemical space expands, the existing structure-based virtual screening …