Prediction of Antimicrobial Peptides Using Machine Learning Approach

---

作者

Mushtaq Ahmad¹ , Prabha Garg¹

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

会议/活动

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)

海报摘要

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.

关键词

Machine learning, Random forest, Antimicrobial peptides, Classifcation model

研究领域

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

参考文献

暂无数据

基金

暂无数据

补充材料

暂无数据

附加信息

利益冲突

No competing interests were disclosed.

数据可用性声明

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

知识共享许可协议

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.