# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 3 Application of CADD Methods in the Development of RAS Inhibitors
### Virtual Screening  
Virtual screening (VS) automatically searches a small-molecule library for structures that potentially bind well to the target biomolecule [71]. VS is a combination of several techniques based on high-throughput screening from millions of lead compounds, i.e., vHTS, to provide a solid foundation for further work. Although the continuous improvement of computer hardware has led to an unprecedented increase in computational power to screen as many compounds as theoretically possible, it is more recommended to construct optimal small-compound libraries and improve the computational speed and hit rate with various optimization algorithms for virtual drug screening strategies. VS can be broadly divided into two types of screening strategies: receptor (structure)-based and ligand-based approaches [71]. Here, receptor and ligand stand for target proteins (drug targets) and small molecules (drugs), respectively. Structure-based virtual screening (SB-VS), also known as receptor-based virtual screening (RB-VS), is progressed inseparably from known protein structures and binding sites. In particular, this strategy involves molecular docking, structure-based pharmacophore modeling, molecular dynamics simulation, etc. SB-VS sometimes has to enable rapid high-throughput screening at the expense of scoring accuracy, so the induced fit effect and solvation effect are generally ignored. Ligand-based virtual screening (LB-VS) predicts the potential structure of candidate drugs from a set of active compounds known to bind to specific sites on the target protein. The features of screened ligands are extracted, such as structural conformation, charge distribution, and physicochemical properties. Then, the feature library, including molecular fingerprints, pharmacophore models, or matching ideal compounds, is constructed by QSAR. This strategy is mainly used when the structure of the target protein is unknown or when the target site is predicted in reverse, while a minor structural change can lead to a drastic change in molecular activity. For RAS inhibitor discovery, an emerging \"hybrid virtual screening\" method has been employed to extract information from the global features of existing ligand-receptor complexes for similarity-matching to reduce the false positive rate and increase computational efficiency [72; 73]. KRAS\({}^{\text{G12D}}\) mutations resulted in a conformational exchange of exposing the Switch I region to continuously activate the GTP-bound form, which was more susceptible to bind to GEFs (e.g., SOS1). GTPase activity subsequently decreased, leading to the aberrant activation of downstream signaling [74; 75]. [PERSON] et al. developed a strategy to inhibit the guanylate cycle by competitively preventing the inactivated state of KRAS\({}^{\text{G12D}}\) \begin{table}
\begin{tabular}{c c c} \hline \hline
**CADD Methods** & **Results** & **References** \\ \hline Homology modeling & The 3D structure of RASSF2 & [51] \\ \hline Molecular dynamics simulation & The stability of the prediction model & [53] \\ \hline Template-based protein–protein complex structure prediction algorithm (PRISM) & The structure of & [55] \\ \hline AlphaFold & Models of 145 RAS superfamily members & [57] \\ \hline Web server (Sitebound-Web) & Top 10 binding pockets on RASSF2 & [51] \\ \hline Probe-based molecular dynamics (PMD) simulation & Five potential druggable sites (S1–S3, Subsite 1 and Subsite 2) & [53] \\ \hline Fragment-based approach (FTMAP) & Three potential allosteric sites (P1–P3) on RAS & [67] \\ \hline Multiple solvent crystal structures (MSCS) & Eight potential binding sites (Cluster 1–Cluster 8) on HRAS & [69] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CADD applications in RAS-related structure and binding sites identification. (iKRAS\({}^{\rm G12D}\)) from binding to SOS1 [76]. In the first round, flavonoids were selected as ligand sets for LB-VS.