CSM-peptides a Computational Approach to Rapid Identification of Therapeutic Peptides

Carlos H.M. Rodrigues, Anjali Garg, David Keizer, Douglas E.V. Pires & David B. Ascher

Summary: Peptides are attractive alternatives for the development of new therapeutic strategies due to their versatility and low complexity of synthesis. Increasing interest in these molecules has led to the creation of large collections of experimentally characterised therapeutic peptides, which greatly contributes to the development of data-driven machine learning approaches. Here we propose CSM-peptides, a novel machine learning method for the identification of 8 different types of therapeutic peptides. By applying an alternative stepwise greedy feature selection approach, our method has shown to outperform existing methods, achieving up to 0.92 AUC on an independent test set. We anticipate CSM-peptides to be of great value in helping on screening large libraries to identify novel peptides with therapeutic potential.

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