Hierarchical Generation of Molecular Graphs using Structural Motifs
This work addresses limitations in molecular graph generation for drug discovery, particularly for larger molecules, representing a novel method for a known bottleneck.
The paper tackled the problem of generating large molecules by proposing a hierarchical graph encoder-decoder that uses larger structural motifs as building blocks, showing significant performance improvements over previous state-of-the-art methods on tasks including polymers.
Graph generation techniques are increasingly being adopted for drug discovery. Previous graph generation approaches have utilized relatively small molecular building blocks such as atoms or simple cycles, limiting their effectiveness to smaller molecules. Indeed, as we demonstrate, their performance degrades significantly for larger molecules. In this paper, we propose a new hierarchical graph encoder-decoder that employs significantly larger and more flexible graph motifs as basic building blocks. Our encoder produces a multi-resolution representation for each molecule in a fine-to-coarse fashion, from atoms to connected motifs. Each level integrates the encoding of constituents below with the graph at that level. Our autoregressive coarse-to-fine decoder adds one motif at a time, interleaving the decision of selecting a new motif with the process of resolving its attachments to the emerging molecule. We evaluate our model on multiple molecule generation tasks, including polymers, and show that our model significantly outperforms previous state-of-the-art baselines.