Application of Machine Learning for the Prediction of Absorption, Distribution, Metabolism and Excretion (ADME) Properties from Cichorium intybus Plant Phytomolecules

• Published in: Processes Vol. 12; no. 11; p. 2488
• Main Authors: Afzal, Ayesha Hena, Alam, Ozair, Zafar, Sherin, Alam, Mohammad Afshar, Ahmed, Kahksha, Khan, Jalaluddin, Khan, Rahmuddin, Shahat, Abdelaaty A., Alhalmi, Abdulsalam
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.11.2024
• Subjects: Absorption; Acids; Chicory; Cichorium; Cichorium intybus; Correlation analysis; Data analysis; Datasets; Decision trees; Diabetes; Diabetes mellitus; Diabetes therapy; Drug development; Drug discovery; Drug metabolism; Excretion; Glucose; Herbal medicine; Hydrogen bonds; Hypoglycemic agents; Insulin; Learning algorithms; Ligands; Machine learning; Medicinal plants; Medicine; Medicine, Botanic; Medicine, Herbal; Metabolism; Molecular weight; Physiological aspects; Principal components analysis; Python; R&D; Research & development; Support vector machines; Toxicity; Type 2 diabetes; India
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr12112488

Drug discovery is the process by which new drug candidates are discovered and drug development takes place. To enhance the efficiency, accuracy, and speed of the drug discovery process, machine learning (ML) could play a transformative role. For this research study, antidiabetic natural compounds from C. intybus, which is commonly known as chicory, were selected, as they have promising antidiabetic properties that can complement conventional diabetes treatments. A bioactive natural compound dataset was retrieved on the chicory plant using Indian Medicinal Plants, Phytochemistry, and Therapeutics (IMPPAT) public source information. This collected dataset was analyzed for its absorption, distribution, metabolism, and excretion (ADME) properties using the SwissADME online tool. Principal component analysis (PCA) and correlation analysis were performed using trial-version XLSTAT software 2014.5.03 and Python. The obtained dataset from SwissADME was subjected to cleaning, after that, it was used to develop machine learning models, such as support vacuum (SVM) ML, random forest (RF), Naive Bayes (NB), and decision tree (DT). The Lipinski rule of violation was chosen as the target variable. To improve the vitality of the created ADME dataset, PCA, a biplot graph, and correlation analysis were carried out. A large dataset of naturally occurring antidiabetic compounds was used to predict the drug-likeness of ML models that were effectively deployed on heterogeneous ADME datasets. Among all these ML models, DT performed better than the rest of the models.