ech T Press Science Computers, Materials & Continua DOI:10.32604/cmc.2021.017297 Article AntiFlamPred: An Anti-Infammatory Peptide Predictor for Drug Selection Strategies Fahad Alotaibi 1 , Muhammad Attique 2,3 and 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: yaser.khan@umt.edu.pk Received: 26 January 2021; Accepted: 03 April 2021 Abstract: Several autoimmune ailments and infammation-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-infammatory proteins/peptides (“AIP”) are usually very costly and remain time-consuming. Therefore, before wet-lab investigations, it is essential to develop in-silico identifcation models to classify prospective anti-infammatory candidates for the facilitation of the drug development process. Several anti-infammatory 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 effciency. An exceedingly accurate anti- infammatory prediction model is proposed, named AntiFlamPred (“Anti- infammatory 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 coeffcient (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 exten- sive lab-based examinations. Subsequently, it has the potential to assiduously support medical and bioinformatics research. Keywords: Prediction; feature extraction; machine learning; bootstrap aggregation; deep learning; bioinformatics; computational intelligence; anti- infammatory peptides This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.