# 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
to identify four sub-micromolar PL\({}^{\rm{PPro}}\) inhibitors in the same in-house library [339]. Furthermore, a slight variation of the protocol was also employed by [PERSON] et al. to discover a noncovalent cyclic peptide that simultaneously inhibits both SARS-CoV-2 \(\mathrm{M}^{\mathrm{pro}}\) and nrp-1 with an activity in the low nanomolar range [340]. Within this scientific work, pharmacophore constraints were used for scoring peptide poses on \(\mathrm{M}^{\mathrm{pro}}\), while traditional docking scores were used for the nrp-1 screening. A remarkable joint computational work by [PERSON] et al. led to the molecular dynamics-driven design of a structure-based pharmacophore filter, which was then exploited to identify two nanomolar \(\mathrm{M}^{\mathrm{pro}}\) inhibitors among a library of publicly available compounds [341]. A similar approach was exploited by [PERSON] et al., which exploited the combination between MD-based pharmacophore filtering, docking-based virtual screening within the Molecular Operating Environment suite, and MD-based postdocking refinement to identify micromolar SARS-CoV-2 cell entry inhibitors targeting the FP of the spike protein [342]. [PERSON] et al. used protein-ligand interaction fingerprint similarity as a postdocking filter for their double virtual screening on both \(\mathrm{M}^{\mathrm{pro}}\) and RdRp with the Vina program to identify seven compounds inhibiting SARS-CoV-2 replication in cell-based assays among a library of approved drugs [343]. Due to the static nature of molecular docking, which does not consider receptor flexibility, the choice of the input structure is vital for the success rate of a virtual screening [344]. Although molecular dynamics can be a useful posterior refinement of poses, a wrong input conformation of the target macromolecule could prevent the sampling of native-like poses for active compounds, leading to a reduced hit-finding rate [345]. For this reason, multiple conformations of the same receptor derived from MD simulations or experimentally solved in different conditions can be used in parallel in a process defined as ensemble docking (ED) [346]. When this approach is used, docking calculations are independently run on each structure, with virtual hit compounds being identified either through consensus scoring or a consensus ranking approach [347; 348]. In the case of consensus scoring, the docking score of the same molecule is averaged across the different virtual screenings, with the final ranking based on the consensus score [349]. Differently, consensus ranking involves the selection of top-ranking hit compounds across different virtual screenings, regardless of congruence between scores [350]. A consensus approach can also be utilized to rank molecules based on virtual screening executed on the same receptor structures with different docking protocols [351]. For example, [PERSON] et al. applied a consensus scoring approach to three independently executed virtual screenings through Glide, FRED [352], and Vina software to identify two \(\mathrm{M}^{\mathrm{pro}}\) micromolar inhibitors within the Drugbank database, a library that includes all drugs approved by the Food and Drug Administration (FDA) [353]. [PERSON] et al., instead, employed an ensemble docking approach with the Glide docking software to identify six \(\mathrm{M}^{\mathrm{pro}}\) inhibitors among a library of commercially available peptidomimetic compounds, two of which demonstrated sub-micromolar potency [354]. [PERSON] et al. used a combination of ensemble docking based on QVina2 [355] and postdocking molecular dynamics refinement to identify five \(\mathrm{M}^{\mathrm{pro}}\) micromolar inhibitors within a library of commercially available natural compounds [356]. A mixture of the previous two approaches was exploited by [PERSON] et al.