# _In Silico_ Development of SARS-CoV-2 Non-Covalent Mpro Inhibitors: A Review
## 2 Mpro Inhibitors for SARS-CoV-2
### Leveraging Quantum Mechanics for ADME-Optimised Drugs  
Beyond the initial pre-screening of inhibitors using molecular docking, molecular dynamics (MD) simulations, and other _in silico_ tools, these computational methods offer more profound insights into critical drug properties. They help evaluate drug-likeness, pharmacokinetics, and toxicity profiles at the molecular level--essential factors for optimising new drug candidates [91]. Many inhibitors, particularly small and flexible compounds, such as non-covalent small molecule Mpro inhibitors like ML188 and its potent derivative 23R [93], require a detailed understanding of their structural and electronic properties. Consequently, quantum mechanical studies have become indispensable in drug design. Among these, density functional theory (DFT), a widely applied quantum mechanics (QM) method, plays a crucial role in obtaining quantitative structure-property relationships (QSPR) for inhibitors [94]. By accurately predicting electronic properties, DFT provides atomic-level insights into inhibitors' conformation, reactivity, and interaction with their target proteins. These electronic and structural attributes are closely linked to a drug's ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, influencing potency and stability. However, the inherent flexibility of inhibitors introduces challenges in conformational sampling, which remains a significant hurdle in effective drug design [94]. Since most drug design algorithms operate in three-dimensional (3D) space, understanding the conformational landscape of a molecule is essential. The binding sites of proteins often reside in concave surface regions, requiring inhibitors to adopt specific 3D geometries for optimal binding [95]. However, identifying the bioactive conformations of flexible inhibitors with multiple rotatable bonds or unsaturated ring systems remains a challenge. As summarised by [PERSON] et al., once a molecular scaffold has been identified, three major obstacles must be addressed, namely: (1) developing methods to generate ensembles of a molecule's bioactive conformations, (2) rationally modifying a pre-established bioactive structure, and (3) approximating real solution-phase conformational ensembles in correlation with spectroscopic data, such as IR, NMR, and UV-vis spectroscopy [96]. While crystal structures provide snapshots of single conformations, they fail to capture the complete flexibility of these molecules. Advanced computational methods have been developed to generate conformational ensembles that more accurately represent bioactive states to bridge this gap. Robust QM-based conformational sampling techniques have become invaluable, offering insights into a wide range of conformer clusters and their associated properties, as illustrated in Figure 15[94]. These methods enhance our understanding of an inhibitor's structural dynamics and provide predictive spectroscopic data. For example, shifts in IR, NMR, and UV-Vis spectra derived from DFT calculations have been correlated with inhibitory potency in the case of tyrosine kinase inhibitors (TKIs), where optical spectral differences between the global minimum and crystallographic conformations aligned with activity trends [97]. However, this correlation has thus far been observed only in TKIs, and its direct applicability to structurally and mechanistically distinct systems--such as SARS-CoV-2 Mpro inhibitors--remains to be established. Therefore, while the approach holds conceptual promise, especially for flexible molecules like ML188, it should currently be regarded as exploratory in the context of Mpro-targeted drug discovery. Notably, variations in optical spectra between an inhibitor's global minimum structure and its crystal structure often correlate with its potency--where larger spectral shifts (depicted in orange in Figure 16) correspond to higher inhibitory efficacy, reflected by a more negative In(IC\({}_{50}\)) function [97].