用基因表达联合控制分子生成，精准设计个性化药物。
Gene Expression-Informed Jointly Controlled Generative Modeling for Precision Molecular Design
- 融合基因表达、分子结构文本和化学性质数值，统一建模生成
- 在多个指标上超越现有方法，且具备强泛化能力
- 适合药物发现与个性化医疗研究者使用
精准分子设计旨在通过联合控制多种条件（如生物相关性与分子设计策略）发现个性化药物候选。生物相关性反映疾病或扰动条件下细胞的功能状态，而分子设计策略则提供结构意图与性质优化的补充指导。本研究提出JoPMol，一种联合控制的精准分子生成模型，将基因表达谱编码的生物状态、文本表达的分子结构信息以及数值量化的化学性质统一整合于一个建模框架中。该方法可实现多条件协同下的候选分子生成与优化。实验结果表明，JoPMol在多个评估指标上均优于当前最优方法，且在迁移任务与基于生物学的模拟场景中展现出强泛化能力，验证了其在精准分子设计中的有效性。源代码已公开于 https://github.com/hala-yh/JoPMol。
原文摘要 · Abstract (English)
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.
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