Introduction: The volume of three-dimensional structural information of macromolecules and the number of computational tools to predict binding modes and affinities of molecular complexes are increasing daily. Molecular docking is a rational structural approach employed to predict thermodynamic parameters based on molecular recognition between two or more molecules. In addition, docking studies have become very important for therapeutic applications in modern structure-based drug design because this computational tool uses few economic resources. However, they omit many biological conditions that critically influence small and macromolecular structural motions. To mimic physiological conditions, it is necessary to consider other environmental factors, such as the presence of water molecules and the flexibility of ligands and side chain residues of proteins. Furthermore, molecular dynamics simulations have been coupled with docking procedures to expand the boundaries and obtain more reliable information. Areas covered: In this article, we review current advances in protein-small molecule docking and possible future directions. Expert opinion: Docking studies include many conformations to predict binding free energies (scoring functions) and to search (scoring sampling) for the most representative binding conformations. Therefore, several biological properties, from side chain residues to complete protein motions, have been included in docking studies to improve theoretical predictions.

Among targets selected for studies aimed at identifying potential inhibitors against COVID-19, SARS-CoV2 main proteinase (M^pro) is highlighted. M^pro is indispensable for virus replication and is a promising target of potential inhibitors of COVID-19. Recently, monomeric SARS-CoV2 M^pro, drug repurposing, and docking methods have facilitated the identification of several potential inhibitors. Results were refined through the assessment of dimeric SARS-CoV2 M^pro, which represents the functional state of enzyme. Docking and molecular dynamics (MD) simulations combined with molecular mechanics/generalized Born surface area (MM/GBSA) studies indicated that dimeric M^pro most significantly impacts binding affinity tendency compared with the monomeric state, which suggests that dimeric state is most useful when performing studies aimed at identifying drugs targeting M^pro. In this study, we extend previous research by performing docking and MD simulation studies coupled with an MM/GBSA approach to assess binding of dimeric SARS-CoV2 M^pro to 12 FDA-approved drugs (darunavir, indinavir, saquinavir, tipranavir, diosmin, hesperidin, rutin, raltegravir, velpatasvir, ledipasvir, rosuvastatin, and bortezomib), which were identified as the best candidates for the treatment of COVID-19 in some previous dockings studies involving monomeric SARS-CoV2 M^pro. This analysis identified saquinavir as a potent inhibitor of dimeric SARS-CoV2 M^pro; therefore, the compound may have clinical utility against COVID-19. Graphical abstract