Hierarchical Graph-to-Graph Translation for Molecules

11 Jun 2019  ·  Wengong Jin, Regina Barzilay, Tommi Jaakkola ·

The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving the encoding of substructure components with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its attachment to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model significantly outperforms previous state-of-the-art baselines.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Drug Discovery DRD2 HierG2G Diversity 0.192 # 1
Success 85.9% # 1
Drug Discovery QED HierG2G Diversity 0.477 # 1
Success 76.9% # 1


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