## 3 Results and Discussion
### Molecular Docking Analysis  
The docking scores of 1500 small molecules from molecular docking in Autodock Vina are listed in Table S1. While docking scores provide an initial indication of potential binding interactions, they are supplemented with molecular dynamics (MD) simulations and MM/PBSA calculations in this study to more accurately assess the stability and binding affinity of the small molecules with the target protein. From 1500 small-molecule ligands, the first 820 were selected based on drug likeness and similarity. To be more precise, molecules that showed a 2D Tanimoto coefficient higher than 0.65 in relation to the reference molecule designated as 6 MZ were eliminated. Moreover, [PERSON]'s Rule of Five was followed to establish the drug-likeness criteria. These 820 molecules were docked to the m6A reader protein active site using Geom X mode. After the results were obtained, 92 compounds with the highest Surflex-GeomX score were examined visually for the following interactions: (1) hydrogen bond interactions with residues SER378, ASN363, and ASN367, (2) interactions with aromatic residues TsRP377, TRP428, and hydrophobic residue LEU439, (3) surface complementarity, and (4) other interactions. After visual inspection, 15 compounds were selected, which are listed in Table 1. Note that visual inspection was preferred over relying solely on the best docking scores to select the 15 compounds. Reasons for this include the following: (1) visual inspection enables a more nuanced assessment, considering factors such as the precise fit within the binding pocket and potential off-target interactions, whereas docking scores often oversimplify the complex interactions within biological systems. (2) We can recognize subtle yet critical features that automated scoring systems might overlook, such as specific bonding patterns or functional group orientations. (3) Visual inspection serves as a vital validation step, ensuring selected compounds have the highest potential for success by evaluating factors beyond numerical scores, such as dynamic interactions and solvent effects.  
The complexes of these 15 ligands with YTHDC1 were visualized using USCF Chimera (Figure S1) and BIOVIA Discovery Studio (Figure S2). Figure 3 illustrates the 3D structures and 2D interaction plots for the top five small molecules (ZINC65274016, ZINC82121447, ZINC02412146, ZINC12780673, and ZINC6475558) identified as potential inhibitors of the YTHDC1 protein. The interactions between the ligands and the protein are highlighted to show the different types of bonds and forces contributing to the binding affinity and stability of these complexes. Hydrogen bonds are represented by dashed green lines and are significant for the specificity and stability of the ligand-protein complex. In the top five molecules, these bonds are predominantly formed with residues ASN367, ASN363, and ASN367  
SER378. Van der Waals interactions are shown as light green lines, involving non-covalent contacts between the ligand and the protein within the binding pocket. \(\pi\)-\(\pi\) Stacking is depicted by magenta lines, occurring between the aromatic rings of the ligands and the aromatic residues of the protein, particularly TRP377 and TRP428, which enhance the binding strength in the aromatic cage of YTHDC1. \(\pi\)-\(\sigma\) Bonding, represented by purple lines, occurs between the aromatic rings of the ligands and sigma bonds within the protein. \(\pi\)-Alkyl and \(\pi\)-Sulfur/\(\pi\)-Cation/Anion interactions are shown in plum and orange colors, respectively. These interactions occur between the aromatic rings of the ligands and the alkyl groups and sulfur atoms or charged atoms of the protein, further stabilizing the ligand within the binding site.