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Updated: May 8, 2026
08:49
Incorporating Target Protein Structure Flexibility and Dynamics in Computational Drug Discovery Using Ensemble-Based Docking Analysis
Published on: June 20, 2025
An Ensemble Method for Predicting and Designing of Druggable Proteins
Shipra Jain1, Srijanee Gupta1, Gajendra P S Raghava1
1Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Proteomics. Clinical Applications
|May 7, 2026
Summary
This study developed a predictive model to identify druggable proteins, achieving high accuracy in distinguishing therapeutic proteins. The findings aid in discovering new drug candidates and streamline protein-based drug development.
Area of Science:
- Biochemistry and Bioinformatics
- Drug Discovery and Development
Background:
- Proteins and peptides exhibit diverse therapeutic properties, including anticancer and antimicrobial effects.
- Despite therapeutic potential, most discovered proteins fail clinical trials, with few gaining US FDA approval due to challenges in identifying druggable candidates.
Purpose of the Study:
- To systematically investigate the properties of FDA-approved proteins.
- To develop predictive models for identifying druggable proteins.
- To enhance the success rate of protein-based drug discovery.
Main Methods:
- Utilized a dataset of 356 FDA-approved proteins and an equal number of negative controls.
- Employed machine learning models, including Random Forest with SVC-L1 feature selection.
- Incorporated MERCI-based motif analysis to identify exclusive patterns in druggable proteins.
Main Results:
- A Random Forest model achieved an AUC of 0.80 and MCC of 0.61 on validation data.
- An ensemble model combining machine learning and exclusive motifs reached an AUC of 0.92 and MCC of 0.83.
- Identified exclusive motifs characteristic of druggable proteins.
Conclusions:
- Developed "ThPPred", a web server and standalone package for predicting and designing druggable proteins.
- The proposed methods and tools aim to assist the scientific community in protein-based drug discovery.
- Facilitates identification of promising protein candidates for therapeutic development.
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