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Letters in Drug Design & Discovery

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ISSN (Print): 1570-1808
ISSN (Online): 1875-628X

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Research Article

Identification of Natural Compounds with Analgesic and Antiinflammatory Properties Using Machine Learning and Molecular Docking Studies

Author(s): Mohammad Firoz Khan*, Ridwan Bin Rashid and Mohammad A. Rashid*

Volume 19, Issue 3, 2022

Published on: 28 July, 2021

Page: [256 - 262] Pages: 7

DOI: 10.2174/1570180818666210728162055

Price: $65

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Abstract

Background: Natural products have been a rich source of compounds for drug discovery. Usually, compounds obtained from natural sources have little or no side effects, thus searching for new lead compounds from traditionally used plant species is still a rational strategy.

Introduction: Natural products serve as a useful repository of compounds for new drugs; however, their use has been decreasing, in part because of technical barriers to screening natural products in highthroughput assays against molecular targets. To address this unmet demand, we have developed and validated a high throughput in silico machine learning screening method to identify potential compounds from natural sources.

Methods: In the current study, three machine learning approaches, including Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Machine (GBM) have been applied to develop the classification model. The model was generated using the cyclooxygenase-2 (COX-2) inhibitors reported in the ChEMBL database. The developed model was validated by evaluating the accuracy, sensitivity, specificity, Matthews correlation coefficient and Cohen’s kappa statistic of the test set. The molecular docking study was conducted on AutoDock vina and the results were analyzed in PyMOL.

Results: The accuracy of the model for SVM, RF and GBM was found to be 75.40 %, 74.97 % and 74.60 %, respectively, which indicates the good performance of the developed model. Further, the model has demonstrated good sensitivity (61.25 % - 68.60 %) and excellent specificity (77.72 %- 81.41 %). Application of the model on the NuBBE database, a repository of natural compounds, led us to identify a natural compound, enhydrin possessing analgesic and anti-inflammatory activities. The ML methods and the molecular docking study suggest that enhydrin likely demonstrates its analgesic and anti-inflammatory actions by inhibiting COX-2.

Conclusion: Our developed and validated in silico high throughput ML screening methods may assist in identifying drug-like compounds from natural sources.

Keywords: Natural products, inflammation, cyclooxygenase, machine learning, molecular docking, high throughput screening.

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