# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 3 Application of CADD Methods in the Development of RAS Inhibitors
### Identification of Binding Sites  
In drug discovery, the identification of potential binding sites for small molecules is critical for the 3D structural model of the target protein. This process can be achieved by using a variety of methods to calculate and identify binding sites. Evaluating the energy, volume, and shape of potential binding sites can reflect the binding capacity of drugs [51; 59]. New algorithms such as Phosphinder [60], LPIcom [61], Sitehound-Web [62], and GenProBiS [63] provide the recognition function of ligand binding sites in protein structures. In the study on RASSF2, [PERSON] et al. [51] derived the potential binding sites of RASSF2 via Sitehound-Web after predicting the 3D structure of the RASSF2 protein. The efficiency of the binding sites can be verified by measuring the energy range and volume of the pockets. These web servers can be used to conveniently discover the potential binding sites of the predicted structures. Probe-based molecular dynamics (PMD) simulation is a common method for discovering potential binding sites of target proteins by adding probe molecules to the conventional MD simulation process. Potential drug targets are identified based on the frequency of contact between the target site and the molecular probe relative to the druggability of the site [64]. In a study on the binding hotspots of KRAS, [PERSON] et al. [65] investigated the surface of KRAS using PMD simulation to evaluate the probability of interaction with organic molecules. Among eight potential druggable sites, five constitute the ligand binding pockets parallel to experimental results (Figure 3, Table 1). Overall, PMD simulation can predict the potential binding sites of various proteins by simply modifying the probe. \begin{table}
\begin{tabular}{c c c} \hline
**Pocket** & **Constituents** & **Location** \\ \hline S1 + Subsite 1 & V7, L56, M67, K5, D54, T74, Y71, & In the core \(\beta\)-strand region \\  & E37, D38 & behind Switch II \\ \hline S2 & V7, V9, G60, F78, M72, Q99, I100 & Near Switch II and \(\alpha\)3 \\ \hline S3 &
\begin{tabular}{c} D105, S106, D107, D108, M111, \\ E162, Q165, H166 \\ \end{tabular} & Between L7 and \(\alpha\)5 \\ \hline Subsite 2 & D30, D33, D38, S39, Y40, I21, I36 & At the back of Switch I \\ \hline \end{tabular}
\end{table} 
Table 1: Constituents and location of experimentally identified pockets composed by potentially druggable sites (S1–S3, Subsite 1 and Subsite 2) from PMD simulation on KRAS. The multiple solvent crystal structures (MSCS) method employs organic solvent molecules to detect and characterize ligand binding sites on proteins. In MSCS, the X-ray crystal structure of the target protein is dissolved in different organic solvents. Organic solvent molecules that accumulate at specific sites on the protein indicate that they are potential sites for molecular interactions [68]. In a hot spot analysis of RAS GTPase surface binding sites, MSCS helped [PERSON] et al. [69] to identify the potential binding sites of HRAS-GppNHp protein-protein interactions. Eight potential binding sites were obtained from different conformations under the crystal structure in the solvent (Figure 5, Table 3). However, MSCS is limited by its reliance on the X-ray crystal structure of the target protein. In addition to predicting the structure of the target protein, AI can also be applied to accurately discover the potential binding sites on the target protein [70]. Discovering potential binding sites with AI will predict the binding ability of the binding sites to the ligands by narrowing the selection range of the target ligands, which is a primary direction for drug design. Table 4 shows the application of CADD in RAS-related structure and binding site identification. Figure 5: Surface representation of eight potential binding sites (Cluster 1–Cluster 8) on HRAS from the MSCS method.