TextOmics-Guided Diffusion for Hit-like Molecular Generation

TL;DR

This paper introduces TextOmics, a benchmark linking omics data with molecular descriptions, and ToDi, a diffusion-based generative framework that produces hit-like molecules conditioned on biological and textual data, advancing drug discovery.

Contribution

The paper presents a novel benchmark for integrating omics and molecular descriptions and proposes a new generative model that outperforms existing methods in biologically relevant molecule generation.

Findings

TextOmics effectively links omics data with molecular descriptions.

ToDi outperforms state-of-the-art methods in generating hit-like molecules.

The framework demonstrates strong zero-shot therapeutic molecule generation capabilities.

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.