Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model

• URL: http://arxiv.org/abs/2410.13597v2
• Date: Sat, 24 May 2025 15:57:21 GMT
• Title: Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model
• Authors: Yida Xiong, Kun Li, Jiameng Chen, Hongzhi Zhang, Di Lin, Yan Che, Wenbin Hu,
• Abstract summary: We propose a text-guided multi-property molecular optimization method utilizing transformer-based diffusion language model (TransDLM)<n>By fusing physically and chemically detailed semantics with specialized molecular representations, TransDLM effectively integrates diverse information sources to guide precise optimization.
• Score: 20.250683535089617
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Molecular optimization (MO) is a crucial stage in drug discovery in which task-oriented generated molecules are optimized to meet practical industrial requirements. Existing mainstream MO approaches primarily utilize external property predictors to guide iterative property optimization. However, learning all molecular samples in the vast chemical space is unrealistic for predictors. As a result, errors and noise are inevitably introduced during property prediction due to the nature of approximation. This leads to discrepancy accumulation, generalization reduction and suboptimal molecular candidates. In this paper, we propose a text-guided multi-property molecular optimization method utilizing transformer-based diffusion language model (TransDLM). TransDLM leverages standardized chemical nomenclature as semantic representations of molecules and implicitly embeds property requirements into textual descriptions, thereby mitigating error propagation during diffusion process. By fusing physically and chemically detailed textual semantics with specialized molecular representations, TransDLM effectively integrates diverse information sources to guide precise optimization, which enhances the model's ability to balance structural retention and property enhancement. Additionally, the success of a case study further demonstrates TransDLM's ability to solve practical problems. Experimentally, our approach surpasses state-of-the-art methods in maintaining molecular structural similarity and enhancing chemical properties on the benchmark dataset. The code is available at: https://github.com/Cello2195/TransDLM.

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