# Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics
## 2 Computer Simulations for Rational Drug Design
### Beyond Protein-Ligand Docking: Alternative Strategies for Rational Drug Development  
Despite the indisputable relevance of molecular docking within most SARS-CoV-2 drug discovery campaigns, other approaches were successfully implemented, especially for projects which deviate from the design of a standard small molecule noncovalent binder. For example, [PERSON] et al. developed _Covcalentizer_, an automated pipeline for the conversion of noncovalent binders to irreversible ones, which was successfully applied to the conversion of a SARS-CoV M\({}^{\text{pro}}\) reversible inhibitor to a sub-micromolar SARS-CoV-2 M\({}^{\text{pro}}\) irreversible one [373]. [PERSON] et al. reported the discovery of D-peptides that bind the spike RBD with low nanomolar affinity, hence blocking SARS-CoV-2 infection in cell-based assays. These ACE2-mimicking peptides were selected within the starting library through a combination of structural alignment, MD-based post docking refinement, and binding free energy estimation [374]. Similarly, a series of peptides mimicking the HR2 domain of the spike protein able to prevent SARS-CoV-2 infection in cell-based assays with low micromolar potency were designed through the combination between structural alignment, mutational scanning with the BeAtMuSiC [375] tool, and MD-based postdocking refinement [376]. [PERSON] et al. used Rosetta [377] to rationally design a mAb that recognizes a conserved surface on the spike RBD of various coronaviruses with picomolar binding affinities, thereby strongly inhibiting SARS-CoV-2 replication in cell-based assay [378]. A similar strategy was exploited by [PERSON] et al., which employed Rosetta docking and MD-based postdocking refinement to design an RNA aptamer that binds with picomolar affinity to the spike RBD and inhibits SARS-CoV-2 replication with sub-micromolar potency in cell-based assay [379]. Further, [PERSON] et al. utilized a combination of modeling with Rosetta and docking with RifDock [380] to design ten mini proteins which bind with picomolar affinity to the spike RBD thus inhibiting SARS-CoV-2 infection within cell-based assays [381]. Moreover, [PERSON] et al. combined modeling with Rosetta and computational alanine scanning with Robetta [382; 383] to rationally design \"ACE2 receptor traps\", i.e., engineered proteins that bind the spike RBD with high affinity and neutralize SARS-CoV-2 infection as effectively as clinically used mAbs [384]. As thoroughly discussed in previous paragraphs, many SARS-CoV-2 drug discovery campaigns favored static, time-independent approaches such as docking or structural alignment, over time-dependent methods such as molecular dynamics. This can be attributed to the long calculation times, the reduced conformational sampling capabilities, and the lower accessibility of MD simulations to the general medicinal chemistry audience [331; 385]. Despite these issues, several works demonstrated the potential of using full-fledged MD-based drug discovery pipelines, especially when smart enhanced-sampling strategies are employed [385]. For example, [PERSON] et al. showed how high-throughput supervised molecular dynamics (HT-SuMD) [386], a virtual screening platform based on an enhanced sampling MD protocol, could be successfully exploited for docking fragments to the active site of SARS-CoV-2 M\({}^{\text{pro}}\), overcoming accuracy limitations of most docking protocols [387] in identifying the native-like binding mode for frag-like compounds [388]. Furthermore, the SuMD [389; 390] algorithm (Figure 5) was successfully exploited by [PERSON] et al. to decipher details about the recognition mechanism of Nirmatrelvir upon the SARS-CoV-2 M\({}^{\text{pro}}\) catalytic site before any structural detail was revealed by the drug developer, with successive structural [189] and molecular medicine [198] studies confirming the prediction validity [391]. Moreover, an evolved version of the SuMD protocol was developed by [PERSON] et al.