The end-to-end platform proposed here is illustrated in Figure . We start with an initial hit molecule -which may result from a high-throughput screening, virtual screening, or other method -and serves as a molecular generator's input. Here, we use the ReLeaSE molecular generator , which can be biased via reinforcement learning to generate new molecules with the desired characteristics. Once biased, the generator can create a diverse library that may be filtered by ADMET properties if desired. A docking program is then used to generate reasonable 3D structures of the drug-receptor complexes, which are finally rescored by calculating the binding energies with higher accuracy with DeepAtom. After rescoring, the best molecules may be selected for synthesis and testing or even be reintroduced as new seeds for the next round of optimization.