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System for Efficacy and Cytotoxicity Screening of Inhibitors Targeting Intracellular Mycobacterium tuberculosis
Published on: April 5, 2017
Prediction of QcrB Inhibition as a Measure of Antitubercular Activity with Machine Learning Protocols
Afreen A Khan1, Sannidhi S Poojary1, Ketki K Bhave1
1Department of Pharmaceutical Chemistry, Vasvik Research Centre, Bombay College of Pharmacy, Kalina, Santacruz (E), Mumbai 400 098, India.
Abstract:
It has always been a challenge to develop interventional therapies for Mycobacterium tuberculosis. Over the years, several attempts at developing such therapies have hit a dead-end owing to rapid mutation rates of the tubercular bacilli and their ability to lay dormant for years. Recently, cytochrome bcc complex (QcrB) has shown some promise as a novel target against the tubercular bacilli, with Q203 being the first molecule acting on this target. In this paper, we report the deployment of several ML-based approaches to design molecules against QcrB. Machine learning (ML) models were developed based on a data set of 350 molecules using three different sets of molecular features, i.e., MACCS keys, ECFP6 fingerprints, and Mordred descriptors. Each feature set was trained on eight ML classifier algorithms and optimized to classify molecules accurately. The support vector machine-based classifier using the ECFP6 feature set was found to be the best classifier in this study. Further, screening of the known imidazopyridine amide inhibitors demonstrated that the model correctly classified the most potent molecules as actives, hence validating the model for future applications.
Insights
Developing new tuberculosis treatments is hard. This study used machine learning to design molecules targeting the QcrB protein, showing promise for future drug discovery against Mycobacterium tuberculosis.
Area of Science:
- Medicinal Chemistry
- Computational Biology
- Drug Discovery
Background:
- Tuberculosis (TB) treatment faces challenges due to Mycobacterium tuberculosis's rapid mutation and dormancy.
- The cytochrome bcc complex (QcrB) presents a novel therapeutic target for TB.
- Q203 is the first molecule identified targeting QcrB.
Purpose of the Study:
- To employ machine learning (ML) approaches for designing novel molecules targeting the QcrB protein.
- To develop and optimize ML models for predicting QcrB inhibitors.
- To validate the predictive accuracy of ML models using known compounds.
Main Methods:
- Development of ML models using a dataset of 350 molecules.
- Utilized three molecular feature sets: MACCS keys, ECFP6 fingerprints, and Mordred descriptors.
- Trained and optimized eight ML classifier algorithms for each feature set.
Main Results:
- The support vector machine classifier with ECFP6 fingerprints demonstrated the highest accuracy.
- The developed ML model successfully classified known potent imidazopyridine amide inhibitors as active.
- Model validation confirmed its capability in identifying effective QcrB inhibitors.
Conclusions:
- ML-based approaches are effective for designing novel anti-tubercular agents targeting QcrB.
- The optimized SVM model with ECFP6 features shows potential for future drug discovery efforts.
- This study provides a validated computational framework for identifying new QcrB inhibitors.
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