# 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
for their High-Throughput Virtual Screening (HTVS), based on both ensemble docking and consensus scoring between the FRED and Vina docking programs, that led to the discovery seven micromolar \(\mathrm{M}^{\mathrm{pro}}\) inhibitors among a set of commercially available compounds [357]. Further, a combination of consensus ranking among Autodock, Hybrid, and FlexX and postdocking molecular dynamics refinement was utilized by [PERSON] et al. to virtually screen a library of commercially available compounds and identify two micromolar \(\mathrm{M}^{\mathrm{pro}}\) inhibitors [358]. Similarly, [PERSON] et al. applied both consensus ranking among three independent virtual screenings performed with the Glide, Autodock, and Vina software and postdocking molecular dynamics refinement to identify 14 micromolar \(\mathrm{M}^{\mathrm{pro}}\) inhibitors within the Drugbank database [359]. Another possible solution to cope with inaccuracy in free binding energy determination by traditional scoring functions is to rescore docking poses using more computationally intensive and accurate methods such as Free Energy Perturbation (FEP) [360] or MMGBSA/MMPBSA [361]. The first approach relies on performing a series of alchemical transformations across a set of ligands that need to be evaluated. This conversion cycle allows calculating relative differences in the free binding energy that can be used for a more accurate ranking of hit compounds derived from a virtual screening [362]. The second approach relies instead on correcting the gas phase interaction energy calculated according to the molecular mechanics force field with a term accounting for the desolvation-free energy, where the polar component is estimated either by numerically solving the Poisson-Boltzmann equation (MMPBSA) or through the Generalized Born method (MMGBSA) [363]. Intriguingly, one of the hit compounds identified in the work of [PERSON] et al. was then used by [PERSON] et al. for the FEP-driven design of multiple nanomolar M\({}^{\text{pro}}\) inhibitors [364]. A similar combination of Glide docking and FEP to determine the absolute binding free energy was also employed by [PERSON] et al. to identify 15 micromolar M\({}^{\text{pro}}\) inhibitors within the Drugbank database [365]. The efficacy of FEP in estimating the binding energy of potential M\({}^{\text{pro}}\) inhibitors was also highlighted by a retrospective study by [PERSON] et al. [366]. A multistep virtual screening involving semiflexible docking with Glide, Schrodinger induced-fit docking [367], MD-based postdocking refinement, and binding free energy estimation with the MMGBSA [368] protocol was exploited by [PERSON] et al. to identify one low micromolar nsp15 inhibitor [369]. Although the estimation of thermodynamic properties such as the free binding energy has been a staple point of drug discovery campaigns, both from a computational and an experimental perspective, lately there has been a major interest shift towards the determination of kinetic parameters since they better correlate with in vivo efficacy [370]. Specifically, several MD-based methods have been developed throughout the years to rank compounds based on their predicted residence time, i.e., the time that the ligand spends in the receptor-bound state [371]. Among those, [PERSON] et al. developed Thermal Titration Molecular Dynamics (TTMD), a new method for qualitative estimation of protein-ligand complex stability (Figure 4), which was successfully applied for correctly discriminating tight, low nanomolar binders from weak, micromolar SARS-CoV-2 M\({}^{\text{pro}}\) inhibitors [372]. Figure 4: Workflow of a Thermal Titration Molecular Dynamics (TTMD) simulation. The time-dependent conservation of the native binding mode within a protein–ligand complex of interest is monitored with a scoring function based on interaction fingerprint through a series of short molecular dynamics simulations performed at progressively increasing temperatures.