Antiprotozoal peptide prediction using machine learning with effective feature selection techniques

Periwal, Neha; Arora, Pooja; Thakur, Ananya; Agrawal, Lakshay; Goyal, Yash; Rathore, Anand S.; Anand, Harsimrat Singh; Kaur, Baljeet; Sood, Vikas

doi:10.1016/j.heliyon.2024.e36163

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e36163

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Antiprotozoal peptide prediction using machine learning with effective feature selection techniques

Neha Periwal,

Pooja Arora,

Ananya Thakur

et al.

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2024

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AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules

Selvam,

Balaji,

Sohn

et al. 2024

Pharmaceuticals

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Background/Objectives: Inflammation serves as a vital response to diverse harmful stimuli like infections, toxins, or tissue injuries, aiding in the elimination of pathogens and tissue repair. However, persistent inflammation can lead to chronic diseases. Peptide therapeutics have gained attention for their specificity in targeting cells, yet their development remains costly and time-consuming. Therefore, small molecules, with their stability, low immunogenicity, and oral bioavailability, have become a focal point for predicting anti-inflammatory small molecules (AISMs). Methods: In this study, we introduce a computational method called AISMPred, designed to classify AISMs and non-AISMs. To develop this approach, we constructed a dataset comprising 1750 AISMs and non-AISMs, each annotated with IC50 values sourced from the PubChem BioAssay database. We computed two distinct types of molecular descriptors using PaDEL and Mordred tools. Subsequently, these descriptors were concatenated to form a hybrid feature set. The SVC-L1 regularization method was implemented for the optimum feature selection to develop robust Machine learning (ML) models. Five different conventional ML classifiers were employed, such as RF, ET, KNN, LR, and Ensemble methods. Results: A total of 15 ML models were developed using 2D, FP, and Hybrid feature sets, with the ET model with hybrid features achieving the highest accuracy of 92% and an AUC of 0.97 on the independent test dataset. Conclusions: This study provides an effective method for screening AISMs, potentially impacting drug discovery and design.

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AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules

Selvam,

Balaji,

Sohn

et al. 2024

Pharmaceuticals

View full text Add to dashboard Cite

show abstract

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Antiprotozoal peptide prediction using machine learning with effective feature selection techniques

Cited by 1 publication

References 78 publications

AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules

AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules

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