Using Machine Learning to Fast-Track Peptide Nanomaterial Discovery

Abstract

Peptides can serve as building blocks for supramolecular materials because of their unique ability to self-assemble, offering potential applications in drug delivery, tissue engineering, and nanotechnology. In this review, we describe peptide self-assembly as a sequence- and context-dependent process and its resulting complexity due to the heterogeneity of the sequences and experimental conditions, which makes cross-laboratory reproducibility a serious challenge and standardized reporting a necessity. Given the large number of possible peptide permutations, machine learning (ML) is suitable for navigating the peptide search space with the aim of reducing trial-and-error experimentation and speeding up the discovery of self-assembling peptides. However, we point out that ML is not a point-and-shoot tool that can be applied directly to any problem and requires careful consideration, domain knowledge, and proper data preparation to achieve meaningful results. In addition, we discuss the lack of negative data reported to be the main limiting factor in the effective application of ML. Considering the transformative potential of artificial intelligence, we conclude that grasping the power of large language models and generative approaches, coupled with explainability techniques, will expedite peptide nanomaterials discovery.