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

2026-07-13

AI Summary

This work addresses the challenge in precision drug design of simultaneously satisfying multiple constraints—including disease-relevant gene expression profiles, structural intent conveyed through textual descriptions, and physicochemical properties—by proposing JoPMol, a novel generative model. JoPMol introduces, for the first time, a unified framework that jointly models gene expression data, semantic structural information from text, and numerical chemical properties within a multimodal conditional generation architecture, enabling biology-driven molecular design. Experimental results demonstrate that JoPMol significantly outperforms existing methods in terms of generation quality, biological plausibility, and cross-task generalization, thereby establishing an effective new paradigm for personalized drug discovery.

Abstract

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.

Problem

• precision molecular design
• gene expression
• joint control
• molecular generation
• biological relevance

Innovation

• jointly controlled generative modeling
• gene expression profiles
• precision molecular design
• multi-condition control
• molecular generation