# Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics
## 2 Computer Simulations for Rational Drug Design
### Complementary Strategies to Address Docking Limitations  
Although a very efficient and useful tool, molecular docking is rarely used on its own within SBDD campaigns and, indeed, is most often coupled with other methods to compensate for its weak points, such as neglecting receptor flexibility or the role of solvents [322], thus increasing the virtual screening success rate [323]. Another major limitation is represented by the poor ranking capabilities of classical scoring functions [324], which is the main cause of the high false positive rate of docking-based virtual screenings [325]. Indeed, in order to be universally applicable across different biological targets and computationally efficient enough to evaluate a large number of compounds, scoring functions have some limitations in the physical description of the binding event, which prevent any correlation between docking scores and experimentally determined affinity values [296]. Furthermore, little to no difference in score exists between top-ranking compounds derived from large virtual screening campaigns, making it practically impossible to distinguish active from inactive compounds solely based on the docking score [326]. For these reasons, each docking-based virtual screening cannot be blindly executed and fully automatized, and a careful setup of the experiment must be executed based on the available literature data and the knowledge of the target [326, 327]. For COVID-19, the importance of this common-sense medicinal chemistry practice has been highlighted by the retrospective literature analysis provided by [PERSON] et al., which showcased the poor performances of structure-based virtual screenings solely based on ranking provided by docking scoring functions [323]. A possible solution to the limited physical description of the protein-ligand binding event of docking is to couple it with molecular dynamics (MD) simulations [294, 328]. Molecular dynamics is a computational technique that allows investigating the time-dependent evolution of biological systems following the rules of molecular mechanics, i.e., determining the atomic trajectories by numerically solving [PERSON]'s equation of motion, where forces between the particles and their potential energies are calculated according to molecular mechanical force fields [329]. Due to the heavy computational workload required to run these types of simulations, MD is rarely used for screening purposes, while it is more frequently exploited for the refinement of docking results, i.e., evaluating the pose stability or optimizing the protein-ligand complex geometry for a more accurate estimation of the free binding energy [330, 331]. Regarding the pitfalls of the scoring component of docking programs, one possible strategy is to apply some form of knowledge-based filter upon docking results, in a similar fashion to what would happen if each pose were visually inspected [332]. For example, experimental information about critical protein-ligand interactions required for binding can be encoded within a pharmacophore filter or an interaction fingerprint, both of which can be used as constraints in the pose selection process [333]. In the case of pharmacophore filters, poses are filtered based on their ability to place a given functional group within a defined volume [334, 335], while in the case of protein-ligand interaction fingerprint, the selection is usually based on the similarity between the reference and the query vector, representing the interaction features of the reference compound (a true active) and the investigated molecule respectively [336, 337]. For instance, [PERSON] et al. used a combination of structure-based pharmacophore screening, docking (both performed with the appropriate tools of the Molecular Operating Environment suite), and postdocking molecular dynamics refinement to identify a set of four sub-micromolar M\({}^{\rm{PPro}}\) inhibitors among a database of in-house compounds [338]. The same protocol was successfully exploited by [PERSON] et al.