# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
###### Abstract  
The Rat Sarcoma (RAS) family (NRAS, HRAS, and KRAS) is endowed with GTPase activity to regulate various signaling pathways in ubiquitous animal cells. As proto-oncogenes, RAS mutations can maintain activation, leading to the growth and proliferation of abnormal cells and the development of a variety of human cancers. For the fight against tumors, the discovery of RAS-targeted drugs is of high significance. On the one hand, the structural properties of the RAS protein make it difficult to find inhibitors specifically targeted to it. On the other hand, targeting other molecules in the RAS signaling pathway often leads to severe tissue toxicities due to the lack of disease specificity. However, computer-aided drug design (CADD) can help solve the above problems. As an interdisciplinary approach that combines computational biology with medicinal chemistry, CADD has brought a variety of advances and numerous benefits to drug design, such as the rapid identification of new targets and discovery of new drugs. Based on an overview of RAS features and the history of inhibitor discovery, this review provides insight into the application of mainstream CADD methods to RAS drug design.  
RAS inhibitor; computer-aided drug design; virtual screening; molecular docking; molecular dynamics simulation +
Footnote †: journal: Medical Chemistry and Bioinformatics Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China  
LB-CADD is an indirect drug design method based on a set of active ligands with certain structural characteristics. By modeling and similarity searching, potentially bioactive compounds can be discovered without knowing the 3D structure of the target protein. A quantitative structure-activity relationship (QSAR), a crucial LB-CADD algorithm, converts the chemical structure of the molecule into descriptors to perform statistical operations and quantitative analysis [6; 7].  
In terms of tactics, CADD can be divided into virtual high-throughput screening (vHTS) and de novo drug design [8]. The former requires an existing molecular database for screening, while the latter relies on generative models [9; 10]. vHTS has been essential in industrial and academic drug discovery for decades [11]. Structure-based vHTS requires molecular docking to critically evaluate ligand-receptor affinity and simulate their binding patterns while screening specific biologically active compounds [4]. Ligand-based vHTS analyzes QSAR descriptors or other quantitative features of ligands while screening. The screening efficiency of vHTS depends on the precision of a certain method and could be enhanced by combining structure-based and ligand-based strategies [12]. De novo drug design, on the other hand, is a tactic that is more demanding and less popular than vHTS. It uses computational algorithms to generate compounds from scratch without a molecular database [10]. In structure-based de novo design, small fragments that match the binding site are created and then assembled into feasible compounds with a novel structure. Although rarely mentioned in RAS inhibitor discovery, de novo design may show its advantage in the future with broader exploration in chemical space and with the application of machine learning [13].  
In general, both of these strategies or tactics have been used alone or in combination in drug discovery. This review provides insight into the applications of several common CADD methods in RAS drug design based on an overview of RAS features and the history of inhibitor discovery.