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Workshop: Machine Learning for Drug Discovery (MLDD)

ChemSpacE: Toward Steerable and Interpretable Chemical Space Exploration

Yuanqi Du · Xian Liu · Shengchao Liu · Jieyu Zhang · Bolei Zhou


Discovering new structures in the chemical space is a long-standing challenge and has important applications to various fields such as chemistry, material science, and drug discovery. Deep generative models have been used in \textit{de novo} molecule design to embed molecules in a meaningful latent space and then sample new molecules from it. However, the steerability and interpretability of the learned latent space remains much less explored. In this paper, we introduce a new task named \textit{molecule manipulation}, which aims to align the properties of the generated molecule and its latent activation in order to achieve the interactive molecule editing. Then we develop a method called \textbf{Chem}ical \textbf{Spac}e \textbf{E}xplorer (ChemSpacE), which identifies and traverses interpretable directions in the latent space that align with molecular structures and property changes. ChemSpacE is highly efficient in terms of training/inference time, data, and the number of oracle calls. Experiments show that the ChemSpacE can efficiently steer the latent spaces of multiple state-of-the-art molecule generative models for interactive molecule design and discovery.

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