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Machine learning (ML) algorithms have gained widespread application in constructing computational models for predicting the bioactivity and physicochemical properties of numerous compounds, notably HDAC inhibitors. In this work, 2801 unique compounds with confirmed bioassays on HDAC2 were collected and employed to train ML models to virtually screening and possibly design potential inhibitors for HDAC2. A meticulous 3‐step procedure was first proposed to ensure the hits are within the application domain of the screening models. Through virtual screening of 91 million compounds, 59 structures were suggested by the four highest predictability models as potential HDAC2 inhibitors. The bioactivity of these compounds is further confirmed through a validated docking protocol, wherein 57 out of the 59 active hits proposed by the four models exhibited superior affinity with HDAC2 compared to vorinostat, with docking scores ranging from −10.74 to −7.06 kcal/mol. Lead optimization strategies were then implemented on the top‐performing compound from the molecular docking scores, CID 122648337, enhancing its binding affinity and interactions within the HDAC2 active site. This optimization led to the creation of two novel lead compounds, demonstrating higher affinity to HDAC2 than the initial hits. The stability of lead compounds in the active site was confirmed via MD simulations. [ABSTRACT FROM AUTHOR]

• In this study, we employed virtual screening method combined with drug repurposing. • Optimization of the pharmacophore model combined with molecular dynamics simulation. • The pharmacophore is constructed based on HDAC2 crystals with selective inhibitors. • One of the screened compounds, the antiviral drug DB13696, showed obvious anti-proliferative ability on HepG2 cells with high HDAC2 expression. Histone deacetylase 2 (HDAC2) is a vital therapeutic target for cancer and neurodegenerative diseases. Although some highly effective HDAC2 inhibitors were developed, their serious side effects and toxicity have not been well resolved. Currently, drug repurposing has been an efficient strategy in drug discovery. In this study, a series of in silico simulations and drug repurposing approaches were employed to identify promising HDAC2 inhibitors. We first constructed a receptor-ligand pharmacophore model. Notably, we combined pharmacophore model construction with molecular dynamics simulation, and used dynamic interatomic distances to evaluate the stability of the interactions and the importance of features in pharmacophore. In addition, molecular docking and molecular dynamics simulations were used to perform further screening. The results suggested that compounds DB08464, DB00731 (Nateglinide), DB13930 (Ulixertinib) and DB13696 (Salifungin) stably bound to HDAC2 and produced many favorable interactions. The MTT assay pointed out that one of the screened compounds, the antiviral drug DB13696 , showed obvious anti-proliferative ability on HepG2 cells with high HDAC2 expression, indicating that the calculated methods performed in this study should be reliable. [ABSTRACT FROM AUTHOR]