Abstract
Recently, deep learning has been successfully applied to molecular graph generation. Nevertheless, mitigating the computational complexity, which increases with the number of nodes in a graph, has been a major challenge. This has hindered the application of deep learning-based molecular graph generation to large molecules with many heavy atoms. In this study, we present a molecular graph compression method to alleviate the complexity while maintaining the capability of generating chemically valid and diverse molecular graphs. We designate six small substructural patterns that are prevalent between two atoms in real-world molecules. These relevant substructures in a molecular graph are then converted to edges by regarding them as additional edge features along with the bond types. This reduces the number of nodes significantly without any information loss. Consequently, a generative model can be constructed in a more efficient and scalable manner with large molecules on a compressed graph representation. We demonstrate the effectiveness of the proposed method for molecules with up to 88 heavy atoms using the GuacaMol benchmark.
Highlights
Deep learning has revolutionized the design of novel molecules required for real-world industrial applications
This study aims to improve the scalability of molecular graph generation to large molecules while maintaining the capability of generating chemically valid and diverse molecular graphs
We identified six small substructural patterns that commonly appear between atom pairs in real-world molecules
Summary
Deep learning has revolutionized the design of novel molecules required for real-world industrial applications. Whereas traditional approaches have mostly been based on human knowledge and intuition, the use of deep learning has enabled the autonomous design of molecules by learning from previously accumulated data [1,2,3]. Most existing methods use deep generative models, such as variational autoencoders (VAEs) and generative adversarial networks (GANs). Their capabilities depend on the way of representing a molecule. Such representations include simplified molecular-input line-entry system (SMILES) and molecular graph representation. The SMILES representation has been demonstrated to be useful, recent research tends to employ the molecular graph representation, which is a natural and intuitive way of representing a molecule by regarding its atoms and bonds as nodes and edges, respectively [1]
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