Scaffold-based molecular design using graph generative model
This work addresses the challenge of scaffold-based molecular design for drug discovery, offering a novel method that improves over previous approaches by ensuring scaffold retention and generalization.
The paper tackles the problem of generating new molecules that retain a specific core scaffold, a common need in drug discovery, by proposing a graph generative model that guarantees scaffold retention and achieves high validity, uniqueness, and novelty in generated molecules, with the ability to control multiple molecular properties simultaneously.
Searching new molecules in areas like drug discovery often starts from the core structures of candidate molecules to optimize the properties of interest. The way as such has called for a strategy of designing molecules retaining a particular scaffold as a substructure. On this account, our present work proposes a scaffold-based molecular generative model. The model generates molecular graphs by extending the graph of a scaffold through sequential additions of vertices and edges. In contrast to previous related models, our model guarantees the generated molecules to retain the given scaffold with certainty. Our evaluation of the model using unseen scaffolds showed the validity, uniqueness, and novelty of generated molecules as high as the case using seen scaffolds. This confirms that the model can generalize the learned chemical rules of adding atoms and bonds rather than simply memorizing the mapping from scaffolds to molecules during learning. Furthermore, despite the restraint of fixing core structures, our model could simultaneously control multiple molecular properties when generating new molecules.