Prediction of Antimicrobial Peptides Using Machine Learning Approach

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Authors

Mushtaq Ahmad¹ , Prabha Garg¹

1. Department of Pharmacoinformatics, National Institute of Pharmaceutical Education and Research, S.A.

Conference / event

Artificial Intelligence Solutions for Pharmaceutical Research and Knowledge (AI-SPARK) 2023” held at National Institute of Pharmaceutical Education and Research (NIPER) S.A.S. Nagar, Sector 67, S.A.S., October 2023 (Mohali, India)

Poster summary

Antimicrobial peptides (AMPs) are natural peptides that are part of the innate immune system and have the potential to address antibiotic resistance. A predictive model has been developed to identify novel AMPs. The RF-based model showed the best prediction results when the number of trees in the forest was kept at 75. ChargeD2001, PAAC12, and polarity T13 performed best with the RF classifier, suggesting they play a significant role in a peptide's antimicrobial potential. The model also correctly predicted known AMPs and non-AMPs, allowing for further experimental validation. This model could help design and predict novel potential AMPs for further research.

Keywords

Machine learning, Random forest, Antimicrobial peptides, Classifcation model

Research areas

Medicine, Bioinformatics and Genomics, Computer and Information Science , Biological Sciences

References

No data provided

Funding

No data provided

Supplemental files

No data provided

Additional information

Competing interests

No competing interests were disclosed.

Data availability statement

The datasets generated during and / or analyzed during the current study are available from the corresponding author on reasonable request.

Creative Commons license

Copyright © 2024 Ahmad et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.