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Molecular Attributes Transfer from Non-Parallel Data
  • Shuangjia Zheng, Sun Yat-sen University, Galixir, Singapore
  • Ying Song, Sun Yat-sen University, China
  • Pan Zhang, Sun Yat-sen University, Galixir, Singapore
  • Le Song, Mohamed bin Zayed University of Artificial Intelligence
  • Chengtao Li, Galixir
  • Yuedong Yang, Sun Yat-sen University, China
Document Type

Optimizing chemical molecules for desired properties lies at the core of drug development. Despite initial successes made by deep generative models and reinforcement learning methods, these methods were mostly limited by the requirement of predefined attribute functions or parallel data with manually pre-compiled pairs of original and optimized molecules. In this paper, for the first time, we formulate molecular optimization as a style transfer problem and present a novel generative model that could automatically learn internal differences between two groups of non-parallel data through adversarial training strategies. Our model further enables both preservation of molecular contents and optimization of molecular properties through combining auxiliary guided-variational autoencoders and generative flow techniques. Experiments on two molecular optimization tasks, toxicity modification and synthesizability improvement, demonstrate that our model significantly outperforms several state-of-the-art methods. © 2021, CC BY-NC-SA.

Publication Date
  • Attribute functions; Chemical molecules; Drug development; Generative model; Learn+; Molecular optimization; Parallel data; Property; Reinforcement learning method; Transfer problems

Preprints: arXiv

  • Archived with thanks to arXiv
  • Preprint License: CC by NC-SA
  • Uploaded 24 March 2022
Citation Information
S. Zheng, Y. Song, P. Zhang, L. Song, C. Li, and Y. Yang, "Molecular attributes transfer from non-parallel data," 2021, arXiv:2111.15146