## 4 Conclusions  
In this study, an exhaustive computational investigation was conducted to identify potential small-molecule ligands targeting the m6A reader protein YTHDC1 from a vast repository of compounds housed within the eMolecules database. Through a rigorous high-throughput virtual screening process, five lead ligands--ZINC82121447, ZINC02170552, ZINC65274016, ZINC10763862, and ZINC02412146--were identified based on their remarkable binding scores, ranging from \(-11.03\)\(-9.6\) kcal/mol, and with corresponding binding free energies spanning from \(-114.03\)\(\pm\)\(10.8\) to \(-9.9990\)\(\pm\)\(72.6\)kJ/mol. Subsequent molecular dynamics simulations validated the stability of these ligand-protein complexes, affirming the potential interactions between the ligands and YTHDC1. Notably, these top-ranking ligands exhibited robust hydrogen bond formations with critical residues such as SER378, ASN363, and ASN367, and effectively engaged the aromatic cage of YTHDC1, interacting with residues TRP377 and TRP428. Nevertheless, ZINC82121447 deserves further attention in the future, as the RMSD of the protein is significantly higher than the ones in other cases, and thus, it is of highest binding affinity and flipped inside the binding pocket during the simulation. A detailed MM/PBSA analysis shed light on the underlying energetics governing ligand binding, with van der Waals interactions emerging as the primary driving force behind favorable ligand-protein interactions. While the majority of identified ligands showcased promising binding attributes, four ligands demonstrated unfavorable binding free energies, indicating potential limitations in their interaction with YTHDC1. A comprehensive ADMET profiling of the ligands yielded satisfactory results in accordance with [PERSON]'s rule of five, underscoring their potential as viable drug candidates. We note that docking simulations cannot correctly rank true binders among a large number of decoys (false binders) because the scoring functions used by docking programs are just rough approximations of binding energy [79]. Although the selected five compounds possess quite high docking scores, there is a risk that most if not all of them could be false binders. Due to the qualitative nature of MM/PBSA analysis, the correlation of binding free energy given by MM/PBSA with experimental IC\({}_{50}\) values is not definitive enough. More accurate computational methods such as free energy perturbation could be the next option [80]. We note that the potency of the suggested inhibitors is comparable to existing ones that were verified experimentally; however, the studyhas demonstrated to be an encouraging attempt to identify alternative inhibitors, which are structurally divergent to existing compounds. In the near future, the potency of these proposed compounds may be enhanced via computational approaches together with medicinal chemistry experts [81]. Looking ahead, further study aims may transition from computational predictions to experimental validation, with planned biochemical and structural investigations to verify the efficacy and specificity of the identified ligands in vitro. This holistic approach underscores the immense potential of computational methodologies in accelerating the discovery of novel therapeutics targeting m6A reader proteins, with significant implications for advancing our understanding of RNA epigenetics and its role in disease pathogenesis.