Computational techniques have been applied in the drug discovery pipeline since the 1980s. Given the low computational resources of the time, the first molecular modeling strategies relied on a rigid view of the ligand-target binding process. During the years, the evolution of hardware technologies has gradually allowed simulating the dynamic nature of the binding event. In this work, we present an overview of the evolution of structure-based drug discovery techniques in the study of ligand-target recognition phenomenon, going from the static molecular docking toward enhanced molecular dynamics strategies.

Unquestionably, water appears to be an active player in the noncovalent protein–ligand binding process, as it can either bridge interactions between protein and ligand or can be replaced by the bound ligand. Accordingly, in the last decade, alternative computational methodologies have been sought with the aim of predicting the position and thermodynamic profile of water molecules (i.e., hydration sites) in the binding site using either the ligand‐bound or ligand‐free protein conformation. Herein, we present an alternative approach, named AquaMMapS, that provides a three‐dimensional sampling of putative hydration sites. Interestingly, AquaMMapS can post‐inspect molecular dynamics (MD) trajectories obtained from different MD engines using indifferently crystallographic or docking‐driven structures as a starting point. Moreover, AquaMMapS is naturally integrated into supervised molecular dynamics (SuMD) simulations, presenting the possibility to inspect hydration sites during the ligand–protein association process. Finally, a penalty scoring method, named AquaMMapScoring(AMS), was developed to evaluate the number and nature of the water molecules displaced by a ligand approaching its binding site during the binding event, guiding a medicinal chemist to explore the most suitable regions of a ligand that can be decorated either with or without interfering with the interaction networks mediated by water molecules with specific recognition regions of the protein.

Molecular docking is one of the most widely used computational approaches in the field of rational drug design, thanks to its favorable balance between the rapidity of execution and the accuracy of provided results. Although very efficient in exploring the conformational degrees of freedom available to the ligand, docking programs can sometimes suffer from inaccurate scoring and ranking of generated poses. To address this issue, several post-docking filters and refinement protocols have been proposed throughout the years, including pharmacophore models and molecular dynamics simulations. In this work, we present the first application of Thermal Titration Molecular Dynamics (TTMD), a recently developed method for the qualitative estimation of protein-ligand unbinding kinetics, to the refinement of docking results. TTMD evaluates the conservation of the native binding mode throughout a series of molecular dynamics simulations performed at progressively increasing temperatures with a scoring function based on protein-ligand interaction fingerprints. The protocol was successfully applied to retrieve the native-like binding pose among a set of decoy poses of drug-like ligands generated on four different pharmaceutically relevant biological targets, including casein kinase 1δ, casein kinase 2, pyruvate dehydrogenase kinase 2, and SARS-CoV-2 main protease.