HydrogelFinder: A Foundation Model for Efficient Self‐Assembling Peptide Discovery Guided by Non‐Peptidal Small Molecules

Abstract

Abstract Self‐assembling peptides have numerous applications in medicine, food chemistry, and nanotechnology. However, their discovery has traditionally been serendipitous rather than driven by rational design. Here, HydrogelFinder, a foundation model is developed for the rational design of self‐assembling peptides from scratch. This model explores the self‐assembly properties by molecular structure, leveraging 1,377 self‐assembling non‐peptidal small molecules to navigate chemical space and improve structural diversity. Utilizing HydrogelFinder, 111 peptide candidates are generated and synthesized 17 peptides, subsequently experimentally validating the self‐assembly and biophysical characteristics of nine peptides ranging from 1–10 amino acids—all achieved within a 19‐day workflow. Notably, the two de novo‐designed self‐assembling peptides demonstrated low cytotoxicity and biocompatibility, as confirmed by live/dead assays. This work highlights the capacity of HydrogelFinder to diversify the design of self‐assembling peptides through non‐peptidal small molecules, offering a powerful toolkit and paradigm for future peptide discovery endeavors.

Keywords

• artificial intelligence
• deep generative model
• machine learning
• self‐assembly