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Related Concept Videos

Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
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Nucleophilic substitution reactions of alkyl halides can proceed via an SN1 or an SN2 mechanism. While in SN2 reactions, the nucleophile attacks the substrate simultaneously as the leaving group departs, in SN1 reactions, the substrate first dissociates to give the carbocation intermediate. Various factors such as the structure of the substrate, the strength of the nucleophile, and the nature of the solvent promote one mechanism over the other.
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Drug Discovery: Overview01:26

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Related Experiment Video

Updated: Jun 30, 2026

A Data Integration Workflow to Identify Drug Combinations Targeting Synthetic Lethal Interactions
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COX-2 Inhibitor Prediction With KNIME: A Codeless Automated Machine Learning-Based Virtual Screening Workflow.

Powsali Ghosh1, Ashok Kumar1, Sushil Kumar Singh1

  • 1Pharmaceutical Chemistry Research Laboratory 1, Department of Pharmaceutical Engineering & Technology, Indian Institute of Technology (Banaras Hindu University), Varanasi, India.

Journal of Computational Chemistry
|January 11, 2025
PubMed
Summary

This study presents an automated KNIME workflow to predict cyclooxygenase-2 (COX-2) inhibitors, crucial for treating inflammation-related diseases like cancer and Alzheimer's. The accessible tool requires no coding, enabling rapid drug discovery.

Keywords:
Cyclooxygenase‐2 inhibitorKNIMEdrug discoverymachine learningvirtual screening

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Area of Science:

  • Computational chemistry
  • Drug discovery
  • Bioinformatics

Background:

  • Cyclooxygenase-2 (COX-2) overexpression is linked to inflammation in diseases like cancer, arthritis, and Alzheimer's disease (AD).
  • In silico virtual screening aids drug discovery, but requires machine learning expertise for accurate predictive models.
  • Developing user-friendly tools is essential to broaden the application of computational methods in identifying potential drug candidates.

Purpose of the Study:

  • To develop an automated KNIME workflow for predicting COX-2 inhibitory potential of novel molecules.
  • To build a robust ensemble model using multiple machine learning algorithms and molecular descriptors.
  • To provide an accessible tool for predicting COX-2 inhibitors without requiring coding or machine learning knowledge.

Main Methods:

  • An automated KNIME workflow was created for predicting COX-2 inhibitors.
  • A multi-level ensemble model was constructed using Logistic Regression, K-Nearest Neighbors, Decision Tree, Random Forest, and Extreme Gradient Boosting algorithms.
  • Various molecular and fingerprint descriptors (AtomPair, Avalon, MACCS, Morgan, RDKit, Pattern) were employed.

Main Results:

  • The final ensemble model achieved 90.0% balanced accuracy, 87.7% precision, and 86.4% recall on an external validation set after applicability domain filtering.
  • The workflow demonstrated high predictive performance for COX-2 inhibitor compounds.
  • The developed model effectively integrates diverse machine learning algorithms and molecular descriptors.

Conclusions:

  • The automated KNIME workflow significantly enhances the accessibility of in silico drug discovery for COX-2 inhibitors.
  • The tool empowers users, from beginners to experienced KNIME users, to predict potential inhibitors efficiently.
  • This approach facilitates broader research and innovation in developing novel therapeutics for COX-2 related conditions.