Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer
<|reference_start|>Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer: 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.<|reference_end|>
@article{kong2024molecule,
    title={Molecule Design by Latent Prompt Transformer},
    author={Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu,
  Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian
  Wu},
    journal={arXiv preprint arXiv:2402.17179},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.17179},
    primaryClass={cs.LG q-bio.BM}
}