Searching the vast chemical space for drug-like and synthesizable molecules with high binding affinity to a protein pocket is a challenging task in drug discovery. Recently, molecular deep generative models have been introduced which promise to be more efficient than exhaustive virtual screening, by directly generating molecules based on the protein structure. However, since they learn the distribution of a limited protein-ligand complex dataset, the existing methods struggle with generating novel molecules with significant property improvements. In this paper, we frame the generation task as a Reinforcement Learning task, where the goal is to search the wider chemical space for molecules with desirable properties as opposed to fitting a training data distribution. More specifically, we propose TacoGFN, a Generative Flow Network conditioned on protein pocket structure, using binding affinity, drug-likeliness and synthesizability measures as our reward. Empirically, our method outperforms state-of-art methods on the CrossDocked2020 benchmark for every molecular property (Vina score, QED, SA), while significantly improving the generation time. TacoGFN achieves in median docking score and in Novel Hit Rate.
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