# Computer-Aided Drug Design Boosts RAS Inhibitor Discovery
## 3 Application of CADD Methods in the Development of RAS Inhibitors
### Molecular Dynamics (MD) Simulation  
The MD simulation is endowed with a temporal dimension in which the dynamic interactions at the atomic and molecular levels can be traced over a period of time [95]. Compared to the static function of the structure determined by X-ray crystallography or cryo-electron microscopy, the MD simulation builds the time function of position and velocity for each atom with ideal environmental variables and initial flexible conformations evolving to final molecular conformations with lower energy or ligand-acceptor interaction patterns. In the MD simulation, a number of conditions should be tightly controlled, including the incorporation of mutations/modifications, the selection of ligand receptors, the imposition of perturbations, etc. Since the initial conditions are limited, the errors caused by the integration process in the simulation accumulate over the time dimension and cannot be completely eliminated. Therefore, it is necessary to improve the accuracy by optimizing the algorithms, exerting appropriate molecular mechanical force fields, and setting suitable parameters. In this regard, the three basic parameters determine the accuracy, such as the simulation range (regional treatments are often required), the time step (should be smaller than the minimum period of oscillation of the particle system, about \(10^{-15}\) s), and the total time duration (must cover the period of natural interaction dynamics, \(10^{-9}\)-\(10^{-6}\) s or longer). In classical MD simulation, the trajectories of molecules are traced by solving the Newtonian equations of motion of the interacting particle system to approximate the quantum mechanical model: the kinematic parameters of the atom are determined via interaction forces with given atomic coordinates and random initial velocities. The crucial kinetic steps are often located in transition states with high free energy and are difficult to be sampled, especially in complicated protein systems with heterogeneous states, complicated interactions, and undefinable solvent effects. To solve these problems, various algorithms should be carefully selected and optimized. The nudged elastic band (NEB) algorithm can calculate the transition state of protein structures within the minimum energy path (MEP) between different conformations [96]. Similarly, [PERSON] et al. [96] used the well established Markov state model (MSM) method to describe long-time dynamics based on the transformation between Markovian substable conformations [97]. The accelerated MD (aMD) simulation method has been used in previous studies to calculate the short time scale in classical MD simulation [98]. Various MD simulation programs, such as GROMACS, AMBER, CHARMM, and NAMD, can evaluate the ligand-receptor binding properties in terms of RMSD, root-mean-square fluctuation (RMSF), interaction forces, and the energy of the complex system, etc. The force field of MD represents a potential energy function consisting of the functional form and the parameter sets. The parameter sets depend on the atom types of the MD molecular systems and are transferable based on the structural similarity of the molecules. For apo structures and polymers of macromolecular systems in biochemistry, optimized potentials for liquid simulations (OPLS), assisted model building with energy refinement (AMBER), and chemistry at Harvard macro-molecular mechanics (CHARMM) are common all-atom force fields with higher simulation accuracy, while Groningen molecular simulation (GROMOS) is a united-atom force field with higher computational efficiency. MD simulation can break the bottleneck in determining protein structures and molecular interactions, especially in drug discovery [99]. In the SOS-induced nucleotide exchange process of the RAS system, MD simulation identifies the stable binding site of SRJ23 in the KRAS4B-SRJ23 (benzylidene derivative of AGP) complex [100; 101].