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Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides
Yiquan Wang1,2, Yahui Ma1, Yuhan Chang2
1Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi 830049, China.
Biology
|December 30, 2025
Summary
Diffusion models accelerate drug discovery by designing small molecules and therapeutic peptides. Overcoming data scarcity and validation challenges is key to realizing their full potential in automated therapeutic engineering.
Area of Science:
- Computational chemistry
- Biotechnology
- Drug discovery
Background:
- Diffusion models are a powerful generative AI framework.
- Traditional drug discovery is slow and expensive.
- AI is transforming therapeutic design.
Purpose of the Study:
- Compare diffusion models for small molecule and peptide design.
- Analyze modality-specific adaptations and challenges.
- Highlight shared hurdles in AI-driven drug discovery.
Main Methods:
- Systematic review of diffusion model applications.
- Analysis of iterative denoising in different molecular contexts.
- Dissection of design objectives and validation strategies.
Main Results:
- Small molecule design excels at structure-based ligand generation but faces synthesizability issues.
- Peptide design focuses on functional sequences and de novo structures, challenged by stability and immunogenicity.
- Shared challenges include limited data, scoring function accuracy, and experimental validation.
Conclusions:
- Bridging modality-specific gaps is crucial for diffusion models.
- Integration into Design-Build-Test-Learn (DBTL) platforms is essential.
- Diffusion models can enable on-demand engineering of novel therapeutics.
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