Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer
CoRR(2024)
摘要
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.
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