Dual-Space Optimization: Improved Molecule Sequence Design by Latent
  Prompt Transformer
Deqian Kong Yuhao Huang Jianwen Xie Edouardo Honig Ming Xu Shuanghong Xue Pei Lin Sanping Zhou Sheng Zhong Nanning Zheng Ying Nian Wu
Designing molecules with desirable properties, such as drug-likeliness and
high binding affinities towards protein targets, is a challenging problem. In
this paper, we propose the Dual-Space Optimization (DSO) method that integrates
latent space sampling and data space selection to solve this problem. DSO
iteratively updates a latent space generative model and a synthetic dataset in
an optimization process that gradually shifts the generative model and the
synthetic data towards regions of desired property values. Our generative model
takes the form of a Latent Prompt Transformer (LPT) where the latent vector
serves as the prompt of a causal transformer. Our extensive experiments
demonstrate effectiveness of the proposed method, which sets new performance
benchmarks across single-objective, multi-objective and constrained molecule
design tasks.