peptidy: A light-weight Python library for peptide representation in machine learning

- Researchers have introduced peptidy, a lightweight Python library that facilitates converting peptides into numerical representations suitable for machine learning. This tool could accelerate peptide discovery and optimization in various fields such as drug discovery and food technology.

- Key features of peptidy include: no external dependencies, seamless integration into modern Python environments, support for a range of encoding strategies suitable for both predictive and generative machine learning, and support for post-translational modifications like phosphorylation, acetylation, and methylation.

- The library implements five popular peptide encoding methods, including peptide descriptors, amino acid descriptors, BLOSUM62 encoding, one-hot encoding, and label encoding. All representations except global descriptors are also suitable for generative deep learning.

- The authors benchmarked peptidy representations using literature data to predict antimicrobial activity and toxicity. Results showed that different encoding strategies have varying performance depending on the task and metric, emphasizing the value of experimenting with alternative representations when training peptide machine learning models.

- Peptidy is accompanied by extensive documentation and tutorials to facilitate accessibility. It is also open-sourced to allow feedback from researchers and extend its capabilities. The package can be easily installed via pip, and the installation instructions, Python code, and benchmarking datasets are available on GitHub.

💻Code:📜Paper:#peptidy

#MachineLearning #PeptideRepresentation #PythonLibrary #DrugDiscovery #FoodTechnology