Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer

• URL: http://arxiv.org/abs/2402.17179v1
• Date: Tue, 27 Feb 2024 03:33:23 GMT
• Title: Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer
• Authors: Deqian Kong, Yuhao Huang, Jianwen Xie, Edouardo Honig, Ming Xu, Shuanghong Xue, Pei Lin, Sanping Zhou, Sheng Zhong, Nanning Zheng, Ying Nian Wu
• Abstract summary: 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 shifts the generative model and the synthetic data towards regions of desired property values. Our experiments demonstrate effectiveness of the proposed method, which sets new performance benchmarks across single-objective, multi-objective and constrained molecule design tasks.
• Score: 78.47949363282868
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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