Open Access

ARTICLE

AntiFlamPred: An Anti-Inflammatory Peptide Predictor for Drug Selection Strategies

Fahad Alotaibi¹, Muhammad Attique^2,3, Yaser Daanial Khan^2,*

1 Department of Information System, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia
2 Department of Computer Science, University of Management and Technology, Lahore, 54000, Pakistan
3 Department of Information Technology, University of Gujrat, Gujrat, 50700, Pakistan

* Corresponding Author: Yaser Daanial Khan. Email:

(This article belongs to the Special Issue: Digital Technology and Artificial Intelligence in Medicine and Dentistry)

Computers, Materials & Continua 2021, 69(1), 1039-1055. https://doi.org/10.32604/cmc.2021.017297

Received 26 January 2021; Accepted 03 April 2021; Issue published 04 June 2021

Abstract

Several autoimmune ailments and inflammation-related diseases emphasize the need for peptide-based therapeutics for their treatment and established substantial consideration. Though, the wet-lab experiments for the investigation of anti-inflammatory proteins/peptides (“AIP”) are usually very costly and remain time-consuming. Therefore, before wet-lab investigations, it is essential to develop in-silico identification models to classify prospective anti-inflammatory candidates for the facilitation of the drug development process. Several anti-inflammatory prediction tools have been proposed in the recent past, yet, there is a space to induce enhancement in prediction performance in terms of precision and efficiency. An exceedingly accurate anti-inflammatory prediction model is proposed, named AntiFlamPred (“Anti-inflammatory Peptide Predictor”), by incorporation of encoded features and probing machine learning algorithms including deep learning. The proposed model performs best in conjunction with deep learning. Rigorous testing and validation were applied including cross-validation, self-consistency, jackknife, and independent set testing. The proposed model yielded 0.919 value for area under the curve (AUC) and revealed Mathew’s correlation coefficient (MCC) equivalent to 0.735 demonstrating its effectiveness and stability. Subsequently, the proposed model was also extensively probed in comparison with other existing models. The performance of the proposed model also out-performs other existing models. These outcomes establish that the proposed model is a robust predictor for identifying AIPs and may subsidize well in the extensive lab-based examinations. Subsequently, it has the potential to assiduously support medical and bioinformatics research.

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Keywords

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