AntiCPs-CompML: A Comprehensive Fast Track ML method to predict Anti-Corona Peptides

Abstract

AbstractThis work introduces AntiCPs-CompML, a novel Machine learning framework for the rapid identification of anti-coronavirus peptides (ACPs). ACPs, acting as viral shields, offer immense potential for COVID-19 therapeutics. However, traditional laboratory methods for ACP discovery are slow and expensive. AntiCPs-CompML addresses this challenge by utilizing three primary features for peptide sequence analysis: Amino Acid Composition (AAC), Pseudo Amino Acid Composition (PAAC), and Composition-Transition-Distribution (CTD). The framework leverages 26 different machine learning algorithms to effectively predict potential anti-coronavirus peptides. This capability allows for the analysis of vast datasets and the identification of peptides with hallmarks of effective ACPs. AntiCPs-CompML boasts unprecedented speed and cost-effectiveness, significantly accelerating the discovery process while enhancing research efficiency by filtering out less promising options. This method holds promise for developing therapeutic drugs for COVID-19 and potentially other viruses. Our model demonstrates strong performance with an F1 Score of 92.12% and a Roc AUC of 76% in the independent test dataset. Despite these promising results, we are continuously working to refine the model and explore its generalizability to unseen datasets. Future enhancements will include featurebased and oversampling augmentation strategies addressing the limitation of anti-covid peptide data for comprehensive study, along with concrete feature selection algorithms, to further refine the model’s predictive power. AntiCPs-CompML ushers in a new era of expedited anti-covid peptides discovery, accelerating the development of novel antiviral therapies.