In precision molecular design, the aim is to discover personalized drug candidates through the 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. This study proposes 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 JoPMol GitHub.
Blogger's Review: The introduction of the JoPMol model offers a fresh perspective on precision molecular design by significantly enhancing the efficiency and effectiveness of drug candidate generation through the joint control of biological states and molecular design strategies. This successful validation underscores the potential of data-driven molecular design in modern drug development, promising more innovations for personalized medicine in the future.