Reshaping the discovery of self-assembling peptides with generative AI guided by hybrid deep learning

Supramolecular peptide-based materials have great potential for revolutionizing fields like nanotechnology and medicine. However, deciphering the intricate sequence-to-assembly pathway, essential for their real-life applications, remains a challenging endeavour. Their discovery relies primarily on empirical approaches that require substantial financial resources, impeding their disruptive potential. Consequently, despite the multitude of characterized self-assembling peptides and their demonstrated advantages, only a few peptide materials have found their way to the market. Machine learning trained on experimentally verified data presents a promising tool for quickly identifying sequences with a high propensity to self-assemble, thereby focusing resource expenditures on the most promising candidates. Here we introduce a framework that implements an accurate classifier in a metaheuristic-based generative model to navigate the search through the peptide sequence space of challenging size. For this purpose, we trained five recurrent neural networks among which the hybrid model that uses sequential information on aggregation propensity and specific physicochemical properties achieved a superior performance with 81.9% accuracy and 0.865 F1 score. Molecular dynamics simulations and experimental validation have confirmed the generative model to be 80-95% accurate in the discovery of self-assembling peptides, outperforming the current state-of-the-art models. The proposed modular framework efficiently complements human intuition in the exploration of self-assembling peptides and presents an important step in the development of intelligent laboratories for accelerated material discovery. A generative model guided by a machine-learning-based classifier capable of assessing unexplored regions of the peptide space in the search for new self-assembling sequences.