Deep-learning models in cheminformatics have substantial difficulties in accurately identifying targets because of their intricate nature and the constraints in analysing extensive training datasets ( ). Deep-learning models may encounter problems identifying meaningful patterns within large datasets, resulting in biases in target selection rather than generating new insights ( ). To overcome these limitations, a potential solution is to develop integrated models that merge ligand and target data to construct complete machine learning frameworks ( ). Aligned with this approach, our methodology creates a resilient machine-learning model by fusing molecular docking techniques and sophisticated chemogenomic models.