A comprehensive survey on artificial intelligence for biomolecule design
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PUBLIC SUMMARY
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Generative AI is reshaping molecule design across drug discovery, materials science, and synthetic biology.
The central dogma framework organizes AI methods for DNA, RNA, proteins, drug-like small molecules, glycans, lipids, and biomaterials.
Key advances, challenges, and future directions in AI-driven molecular engineering are systematically reviewed.
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ABSTRACT
Artificial Intelligence (AI), particularly Generative AI (GenAI), has revolutionized molecule design by enabling the creation of novel and complex molecular structures, advancing drug discovery, materials science, and synthetic biology. This survey provides a comprehensive exploration of AI-driven molecule design, categorized within and beyond the central dogma of molecular biology. We first introduce the foundational concepts of GenAI, focusing on architectures such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models (DMs), which underpin molecular design applications. Then, we examine how GenAI facilitates molecule design within the central dogma, addressing DNA, RNA, and protein design. Beyond the central dogma, we explore applications in designing small molecules, lipids, glycans, and other biomaterials. Finally, we conclude by discussing current challenges, emerging trends, and potential future directions for GenAI in molecule design. Overall, this survey aims to equip researchers with a thorough understanding of this rapidly-evolving field and inspire novel developments and breakthroughs in GenAI for molecule design. -
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REFERENCES
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Yang G., Li J., Wang Q., et al. (2026). A comprehensive survey on artificial intelligence for biomolecule design. The Innovation Life 4:100196. https://doi.org/10.59717/j.xinn-life.2026.100196
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Yang G., Li J., Wang Q., et al. (2026). A comprehensive survey on artificial intelligence for biomolecule design. The Innovation Life 4:100196. https://doi.org/10.59717/j.xinn-life.2026.100196
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Figure 1.
Landscape of GenAI Methodologies.
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Figure 2.
Taxonomy of GenAI Models
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Figure 3.
Design of biomolecules within the central dogma
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Figure 4.
Design of biomolecules beyond the central dogma
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Figure 5.
A general workflow for designing functional biomolecules using GenAI models.
