Computational methods constitute efficient strategies for screening and optimizing potential
drug molecules. A critical factor in this process is the binding affinity between candidate …

The rapid advancement in computational power available for research offers to bring not
only quantitative improvements, but also qualitative changes in the field of biomolecular …

The bioactivity of compounds plays an important role in drug development and discovery.
Existing machine learning approaches have poor generalizability in bioactivity prediction …

Molecular dynamics (MD) simulations are widely applied to estimate absolute binding free
energies of protein–ligand and protein–protein complexes. A routinely used method for …

In drug discovery, the in silico prediction of binding affinity is one of the major means to
prioritize compounds for synthesis. Alchemical relative binding free energy (RBFE) …

Computational free energy-based methods have the potential to significantly improve
throughput and decrease costs of protein design efforts. Such methods must reach a high …

The structure-based technologies most widely used to rank the affinities of candidate small
molecule drugs for proteins range from faster but less reliable docking methods to slower but …

Structure-based methods in drug discovery have become an integral part of the modern
drug discovery process. The power of virtual screening lies in its ability to rapidly and cost …

Data-driven techniques for establishing quantitative structure property relations are a pillar
of modern materials and molecular discovery. Fuelled by the recent progress in deep …

Relative alchemical binding free energy calculations are routinely used in drug discovery
projects to optimize the affinity of small molecules for their drug targets. Alchemical methods …