Gene Expression-Informed Jointly Controlled Generative Modeling for Precision Molecular Design

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 this https URL.