Peptides can bind challenging disease targets with high affinity and specificity, offering
enormous opportunities for addressing unmet medical needs. However, peptides' unique …

Many physics-based and machine-learned scoring functions (SFs) used to predict protein-
ligand binding free energies have been trained on the PDBBind dataset. However, it is …

The prediction of drug-target binding affinity (DTA) plays an important role in drug discovery.
Computerized virtual screening techniques have been used for DTA prediction, greatly …

Active learning (AL) has become a powerful tool in computational drug discovery, enabling
the identification of top binders from vast molecular libraries. To design a robust AL protocol …

Drug discovery pipelines nowadays rely on machine learning models to explore and
evaluate large chemical spaces. While including 3D structural information is considered …

In recent years, the application of machine learning (ML), including deep learning (DL), has
experienced exponential growth, which has promoted data-driven discovery in diverse …

Computational approaches for small-molecule drug discovery now regularly scale to
consideration of libraries containing billions of candidate small molecules. One promising …