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.

通过引入 Dual-Space Optimization (DSO) 方法，结合了潜空间采样和数据空间选择来解决分子设计中的难题，本文提出的生成模型采用潜在提示变换器（LPT）的形式，通过优化过程逐渐实现生成模型和合成数据朝着期望的属性值区域移动，并在单目标、多目标和受限分子设计任务中创造了新的性能基准。