TextOmics-Guided Diffusion for Hit-like Molecular Generation

• URL: http://arxiv.org/abs/2507.09982v1
• Date: Mon, 14 Jul 2025 06:56:37 GMT
• Title: TextOmics-Guided Diffusion for Hit-like Molecular Generation
• Authors: Hang Yuan, Chen Li, Wenjun Ma, Yuncheng Jiang,
• Abstract summary: Hit-like molecular generation with therapeutic potential is essential for target-specific drug discovery.<n>We introduce TextOmics, a pioneering benchmark that establishes one-to-one correspondences between omics expressions and molecular textual descriptions.<n>We propose ToDi, a generative framework that jointly conditions on omics expressions and molecular textual descriptions to produce biologically relevant, chemically valid, hit-like molecules.
• Score: 11.308365776402226
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Hit-like molecular generation with therapeutic potential is essential for target-specific drug discovery. However, the field lacks heterogeneous data and unified frameworks for integrating diverse molecular representations. To bridge this gap, we introduce TextOmics, a pioneering benchmark that establishes one-to-one correspondences between omics expressions and molecular textual descriptions. TextOmics provides a heterogeneous dataset that facilitates molecular generation through representations alignment. Built upon this foundation, we propose ToDi, a generative framework that jointly conditions on omics expressions and molecular textual descriptions to produce biologically relevant, chemically valid, hit-like molecules. ToDi leverages two encoders (OmicsEn and TextEn) to capture multi-level biological and semantic associations, and develops conditional diffusion (DiffGen) for controllable generation. Extensive experiments confirm the effectiveness of TextOmics and demonstrate ToDi outperforms existing state-of-the-art approaches, while also showcasing remarkable potential in zero-shot therapeutic molecular generation. Sources are available at: https://github.com/hala-ToDi.

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