# Lessons Learnt from COVID-19: Computational Strategies for Facing Present and Future Pandemics
## 2 Computer Simulations for Rational Drug Design
### CADD Strategies against COVID-19  
The starting point of every SBDD campaign is the identification of a target macromolecule (a protein or a nucleic acid) that is involved in the etiology and or pathogenesis of a disease of interest, whose function can be opportunely modulated through a specifically designed ligand, usually a small organic molecule [171].  
Once the target has been identified, its structure must be retrieved, either through experimental methods such as X-Ray crystallography (XRC, the gold standard) [256], nuclear magnetic resonance (NMR) [257], and cryo-EM [258] or hypothesized through homology modeling or de novo prediction [259].  
Homology modeling involves the use of a homologous protein with a high primary sequence identity with the target as a template for the construction of its three-dimensional model [260; 261]. De novo prediction, instead, does not rely on any information about other proteins' structures and outputs a structural hypothesis that is solely based on the primary sequence of the target of interest [262].  
While the second approach has gained a lot of momentum during the last two years, thanks to its unprecedentedly high accuracy [263; 264], the first one is still relevant in those cases where important structural rearrangements occur between different states of the target functional cycle, other than predicting ligand-bound conformations [265; 266].  
In the context of the COVID-19 pandemic, where the extraordinary effort promoted by the scientific community quickly made several experimentally determined structures available, the relevance of structural modeling was highlighted by the ability to keep up with the high mutation rate of the virus [135; 207], other than providing a useful starting point for drug discovery campaigns for a target whose structure had yet to be elucidated [267; 268]. For example, several studies were conducted to investigate the impact of mutations found in both the spike protein [135; 269; 270; 271; 272; 273] and the main protease [135; 198; 274; 275] of emerging strains on viral fitness and resistance to existing therapies. These studies showed that relatively inexpensive approaches such as homology modeling and positional scanning can be reliable tools to rationalize the origin of the virus [276; 277; 274; 278], quickly track the evolution of the original strain [135; 279; 280], predict the impact of future possible mutations [270; 272] and adjust existing therapeutics tools accordingly [198; 281].  
The huge amount of structural information available on several SARS-CoV-2 drug-gable targets was fertile terrain for various COVID-19 SBDD campaigns [282; 283], both in academia and in industry, with the most effort aimed at hitting well-characterized and pivotal viral targets such as M\({}^{\rm pro}\) or spike [284; 285].  
A remarkable example is represented by the COVID Moonshot Consortium, a drug discovery campaign driven by a collaborative effort among different research groups across the world aimed at targeting the SARS-CoV-2 main protease. This project led to the advancement of novel noncovalent orally available nanomolar M\({}^{\rm pro}\) inhibitors to clinical stage experimentation [286].