De novo PROTAC design using graph-based deep generative models

PROteolysis TArgeting Chimeras (PROTACs) are an emerging therapeutic modality that degrade a protein of interest (POI) by marking it for degradation by the proteasome. They often take on a three-component barbell-like structure consisting of two binding domains and a linker. While a promising modality, it can be challenging to predict whether a new PROTAC will lead to protein degradation as that is dependent on the cooperation of all subunits to form a successful ternary structure. As such, PROTACs remain a laborious and unpredictable modality to design because the functionalities of each component are highly interdependent. Recent developments in artificial intelligence (AI) suggest that deep generative models can assist with the de novo design of molecules displaying desired properties, yet their application to PROTAC design remains largely unexplored. Additionally, while previous AI-based approaches have optimized the linker component given two active domains, generative models have not yet been applied to optimization of the other two – the warhead and E3 ligand. Here, we show that a graph-based deep generative model (DGM) can be used to propose novel PROTAC structures. The DGM follows the approach of GraphINVENT, a gated-graph neural network which iteratively samples an action space and formulates a sequence of steps to build a new molecular graph. We also demonstrate that this model can be guided towards the generation of PROTACs that are predicted to effectively degrade a POI through policy gradient reinforcement learning (RL). Rewards during RL are applied based on a boosted tree surrogate model that predicts a PROTAC's degradation potential for a specific POI, showing that a nonlinear scoring function can fine-tune a deep molecular generative model towards desired properties. Using this approach, we achieve a model where activity against IRAK3 (a pseudokinase implicated in oncologic signaling) is predicted for >80% of sampled PROTACs after RL, compared to 50% predicted activity before any fine-tuning.