These decoys, which are active molecules docked outside the binding site, or active molecules docked in just incorrect poses, possess the same ligand information but dissimilar interaction information. We assign an affinity label based on the docking score, which is normalized between 0 and 3, thereby providing the label with a degree of physical significance and facilitating the regression task done in logarithmic units of potency. In this manner, the models now train on the same ligand bound to the same protein but in different complexes, with different interactions and different assigned affinities. The models are compelled to utilize the full combination of protein and ligand information for accurate predictions. . In the literature, two primary strategies have been identified: the first entails cross-docking all PDBbind compounds and assigning low affinity scores to the resulting virtual complexes , while the second employs decoy compounds sourced from databases such as DUD-E but results show this can be problematic and improvements have been suggested . These decoys are presumed to be inactive due to their topological dissimilarities from the reference compounds, yet they possess analogous physicochemical characteristics. For every drug discovery LO target, we have docked confirmed bioinactive compounds selected from their respective high throughput screening data. Additionally, we have reduced the inactive compound pool by filtering for drug-like properties, thus impeding the models from readily discerning differences between inactive and active compounds. Since we dock the same compound to multiple crystal structures for each target, we keep the docking solution with the best score. We noticed that our 2DCNN model did not benefit from using this data for training, keeping the generated inactive compounds for benchmarking.