While docking programs such as Schrödinger Glide are effectively used to select compounds, binding affinities obtained with the scoring functions available in docking programs, in general, are known to be inaccurate when compared to experimental data.13,14 On the other hand, the recently released DeepAtom method based on convolutional neural networks can predict the binding affinities of ligands with higher accuracy. DeepAtom, however, was trained only with positive samples of structure and binding affinity pairs and was not designed to detect binding poses. Furthermore, inference with DeepAtom takes considerably longer than docking, for example, up to 5 min/molecule in our system, and is not ideal for screening large libraries. Here, we take a mixed approach where we rescore the glide-generated poses with DeepAtom to determine the binding energy. The results for the molecules approved by the ADMET filters are shown in Figure . By comparison, we also display data obtained from 10 independent runs of DeepAtom with the crystal structure for the Vioxx/COX-2 complex (PDBID:5KIR). Most (82) of the generated compounds are predicted to have lower binding energy than the average value obtained for Vioxx (-7.5), illustrating Elion's ability to find new potential drug candidates.