AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules
<b>Background/Objectives:</b> 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...
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MDPI AG
2024-12-01
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| Series: | Pharmaceuticals |
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| author | Subathra Selvam Priya Dharshini Balaji Honglae Sohn Thirumurthy Madhavan |
| author_facet | Subathra Selvam Priya Dharshini Balaji Honglae Sohn Thirumurthy Madhavan |
| author_sort | Subathra Selvam |
| collection | DOAJ |
| description | <b>Background/Objectives:</b> 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). <b>Methods:</b> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">I</mi><mi mathvariant="normal">C</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></semantics></math></inline-formula> 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. <b>Results:</b> 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. <b>Conclusions:</b> This study provides an effective method for screening AISMs, potentially impacting drug discovery and design. |
| format | Article |
| id | doaj-art-3178450c4ea4464d93d28ff81dfcdec5 |
| institution | Kabale University |
| issn | 1424-8247 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Pharmaceuticals |
| spelling | doaj-art-3178450c4ea4464d93d28ff81dfcdec52024-12-27T14:46:06ZengMDPI AGPharmaceuticals1424-82472024-12-011712169310.3390/ph17121693AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small MoleculesSubathra Selvam0Priya Dharshini Balaji1Honglae Sohn2Thirumurthy Madhavan3Computational Biology Laboratory, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203, Tamil Nadu, IndiaComputational Biology Laboratory, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203, Tamil Nadu, IndiaDepartment of Chemistry, Chosun University, Gwangju 501-759, Republic of KoreaComputational Biology Laboratory, Department of Genetic Engineering, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu 603203, Tamil Nadu, India<b>Background/Objectives:</b> 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). <b>Methods:</b> 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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi mathvariant="normal">I</mi><mi mathvariant="normal">C</mi></mrow><mrow><mn>50</mn></mrow></msub></mrow></semantics></math></inline-formula> 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. <b>Results:</b> 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. <b>Conclusions:</b> This study provides an effective method for screening AISMs, potentially impacting drug discovery and design.https://www.mdpi.com/1424-8247/17/12/1693anti-inflammatoryautoimmune diseasesmall moleculesmachine learningk-fold cross-validation |
| spellingShingle | Subathra Selvam Priya Dharshini Balaji Honglae Sohn Thirumurthy Madhavan AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules Pharmaceuticals anti-inflammatory autoimmune disease small molecules machine learning k-fold cross-validation |
| title | AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules |
| title_full | AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules |
| title_fullStr | AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules |
| title_full_unstemmed | AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules |
| title_short | AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules |
| title_sort | aismpred a machine learning approach for predicting anti inflammatory small molecules |
| topic | anti-inflammatory autoimmune disease small molecules machine learning k-fold cross-validation |
| url | https://www.mdpi.com/1424-8247/17/12/1693 |
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