# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 3 Application of CADD Methods in the Development of RAS Inhibitors
### Quantitative Structure-Activity Relationship Study (QSAR)  
QSAR is created by combining mathematical methods of empirical equations commonly used in physical chemistry based on the traditional structure-activity relationship between molecular structure and properties, such as molecules with similar structures having similar properties. Briefly, QSAR consists of the following core steps: experimentally determining the data for various compounds to construct training and test sets; computationally converting the structural formulas into the descriptor data for statistical operations, a process known as the acquisition of molecular descriptors; establishing a statistical model between the molecular properties of interest and the molecular descriptors of the training set; evaluating the obtained model according to various indicators; and attempting to explain the model from a mechanism. Various molecular descriptors reflecting different levels of chemical structure representation have been proposed as the core of QSAR. These levels range from molecular formulas (so-called 1D) and two-dimensional structural formulas (2D) to three-dimensional conformational formulas (3D) and higher formulas that take mutual orientation and time dependence into account (4D or higher) [6].  
The 2D descriptor mainly defines the connectivity of atoms in molecules according to the existence and nature of chemical bonds, which is also called topological representation. This representation contains valuable and straightforward information about the molecular structure and has the advantage of being invariant to the rotation and translation of molecules. Although 2D descriptors cannot be used as unique representations without reconstructing molecules, they can characterize molecules with higher discrimination with well-defined ordered sequences [123]. Furthermore, 3D descriptors based on biological selectivity result from highly specific interactions between the target and ligands, such as hydrogen bonds. The ligand preferences arise mainly from non-covalent field effects imposed by the spatial proximity. The systematic sampling of field differences, such as the CoMFA formulation with the classical and dominant comparative molecular field, provides molecular descriptors suitable for QSAR. The main challenge in performing CoMFA is the alignment protocol for selecting conformation and orientation of the ligands in the training and test sets, which is cumbersome and expensive. However, new QSARs, such as topologically heterogeneous protocols, dramatically simplify reliable predictability [124]. Overall, 3D-QSAR research requires the structural alignment of compounds as the most critical step. Being related to the alignment protocol, the major obstacle in performing CoMFA is precisely the selection of ligands for the training and test groups, as well as the selection of the conformation and orientation of each ligand. In practice, this task often becomes slow and tedious and somewhat temporarily requires higher statistical standards (such as q2) [123]. To overcome this bottleneck, QSAR usually works with with other techniques such as molecular docking.  
As a member of the RAS activator family (RAF), BRAF mutations exhibit markedly increased kinase activity and a high degree of disease severity. The BRAF V600E mutation, one of the most known oncogenic protein kinase mutations, represents an excellent potential therapeutic target. Due to the collaboration between molecular docking techniques and reliable CoMFA and CoMSIA models, three different V600E BRAF inhibitor datasets were generated based on a dataset of 125 compounds using receptor-guided alignment methods and database alignment. Both models show good statistical values and are validated by y-randomization tests. Finally, the newly predicted structure (IIIa) shows a higher inhibitory potency than the previous active compounds in the series (pIC\({}_{50}\) = 6.826) [125].