DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.

Feng Xu, Ran Fu, Qian Cheng, Shuqin Jiang, Jun Yan, Xiangfeng Wang

Abstract readReview

In one paragraph

Review in Science China. Life sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Feng Xu *State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China.

Ran Fu *State Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China.

Qian ChengState Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China.

Shuqin JiangMolBreeding Biotechnology Co., Ltd., Shijiazhuang, 051430, China.

Jun YanState Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China. yanjun@cau.edu.cn.

Xiangfeng WangState Key Laboratory of Maize Bio-Breeding, National Maize Improvement Center, Frontiers Science Center for Molecular Design Breeding, College of Agronomy and Biotechnology, China Agricultural University, Beijing, 100094, China. xwang@cau.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Crop breeding is shifting from selecting naturally occurring variants to actively designing genetic features. Generative artificial intelligence (GenAI) provides a computational foundation for this transition; however, its application in crop systems remains limited. This review bridges this gap by systematically evaluating the translational potential of GenAI for crop molecular design breeding. We examine four major computational architectures and their applications across six key areas relevant to crop breeding: gene regulatory element design, protein engineering, small-molecule generation, gene-editing tool optimization, single-cell modeling, and cross-domain knowledge integration. Building on these advances, we propose the DESIGN framework, which organizes GenAI-enabled breeding into six synergistic capabilities: Discover, Engineer, Simulate, Integrate, Generate, and Navigate, thereby forming a closed-loop system spanning molecular design to field deployment. Finally, we discuss key bottlenecks in data, models, and validation, and outline a roadmap for translating GenAI into breeding practice. This review provides a conceptual and methodological foundation for the emerging paradigm of design-driven crop breeding.

Indexed as

crop breedingDESIGN frameworkgenerative artificial intelligencemolecular design

Identifiers

PMID42886441

DOI10.1038/s41467-022-32818-8

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.