Peptides are small proteins, regularing many important biological processes. They have significant therapeutic potential, thanks to their properties, e.g. microbial, antiviral, or anticancer.
In particular, they offer a promising alternative to traditional antibiotics, addressing the growing crisis of drug resistance.
Accurately predicting peptide properties is essential for drug discovery, and recent research has explored deep learning approaches such as graph neural networks, protein language models, and multimodal ensembles.
However, these methods are often overly complex and lack scalability. They are also brittle and their performance breaks down on new datasets or tasks.
We propose to use molecular fingerprints for this task. They are established feature extraction algorithms from chemoinformatics, primarily applied on small molecules.
We show that they obtain state-of-the-art results on peptide function prediction and can efficiently vectorize larger biomolecules.
This approach is simple, fast, and accurate. We comprehensively measure its robustness on 6 benchmarks and 126 datasets. This unlocks a novel venue in chemoinformatics-based approaches for peptide-based drug design.