Learning to Extend Molecular Scaffolds with Structural Motifs
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Recent advancements in deep learning-based modeling of molecules promise to accelerate in silico drug discovery. There is a plethora of generative models available, which build molecules either atom-by-atom and bond-by-bond or fragment-by-fragment. Many drug discovery projects also require a fixed scaffold to be present in the generated molecule, and incorporating that constraint has been recently explored. In this work, we propose a new graph-based model that learns to extend a given partial molecule by flexibly choosing between adding individual atoms and entire fragments. Extending a scaffold is implemented by using it as the initial partial graph, which is possible because our model does not depend on generation history. We show that training using a randomized generation order is necessary for good performance when extending scaffolds, and that the results are further improved by increasing fragment vocabulary size. Our model pushes the state-of-the-art of graph-based molecule generation, while being an order of magnitude faster to train and sample from than existing approaches.
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