Text-Guided Multi-Property Molecular Optimization with a Diffusion Language Model
This addresses the problem of error propagation in molecular optimization for drug discovery researchers, representing a novel method for a known bottleneck.
The paper tackles molecular optimization for drug discovery by proposing TransDLM, a diffusion language model that uses text descriptions to guide multi-property optimization, surpassing state-of-the-art methods in maintaining structural similarity and enhancing chemical properties on benchmark datasets.
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.