发表机构
South China Normal University; The University of Osaka（华南师范大学; 大阪大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对精确分子设计，提出JoPMol模型，整合基因表达谱、分子结构信息和化学性质，在统一框架下联合控制生成与优化候选分子，实验显示其性能优于现有方法且泛化能力强，为精确分子设计提供有效模型。
AI 中文摘要
精确分子设计旨在通过联合控制多种条件来发现个性化药物候选物。生物相关性反映疾病或扰动条件下的细胞功能状态，分子设计策略提供结构意图和性质优化方面的补充指导。本研究提出JoPMol，一个联合控制的精确分子生成模型，在统一框架中整合基因表达谱编码的生物状态、文本表达的分子结构信息和数值量化的化学性质，实现联合条件控制下候选分子的协同生成与优化。实验表明JoPMol在多个评估指标上优于现有方法，在迁移任务和生物基础模拟场景中具有强泛化能力，验证其对精确分子设计有效，代码公开。
英文摘要
Precision molecular design aims to discover personalized drug candidates through joint control of multiple conditions, such as biological relevance and molecular design strategies. Biological relevance reflects cellular functional states under disease or perturbation conditions, while molecular design strategies provide complementary guidance in terms of structural intentions and property optimization. In this study, we propose JoPMol, a jointly controlled precision molecular generative model that integrates biological states encoded by gene expression profiles with molecular structure information expressed in text, and chemical properties quantified by numerical values within a unified modeling framework. This formulation enables coordinated generation and optimization of candidate molecules under joint condition control. Experimental results show that JoPMol outperforms state-of-the-art methods across multiple evaluation metrics. Moreover, JoPMol demonstrates strong generalization ability in both transfer tasks and biologically grounded simulation scenarios, validating its effectiveness for precision molecular design. The source code is publicly available at https://github.com/hala-yh/JoPMol.
Comments15 pages, 7 figures, 7 tables. Source code: https://github.com/hala-yh/JoPMol
