Welcome to the official repository for MDRIPred, a computational platform developed for predicting inhibitors against drug-tolerant Mycobacterium tuberculosis (H37Rv). The system was designed to identify compounds effective against both replicative and non-replicative phases of tuberculosis under carbon starvation conditions.
This platform supports tuberculosis drug discovery by integrating machine learning, molecular fingerprints, pharmacophore analysis, and structural feature analysis.
Web Server: http://crdd.osdd.net/oscadd/mdri/
Singla, D., Tewari, R., Kumar, A., & Raghava, G. P. S. (2013).
Designing of inhibitors against drug tolerant Mycobacterium tuberculosis (H37Rv).
Chemistry Central Journal, 7, 49.
https://doi.org/10.1186/1752-153X-7-49
https://doi.org/10.5281/zenodo.20081512
Tuberculosis (TB), caused by Mycobacterium tuberculosis (M.tb), remains one of the deadliest infectious diseases worldwide. The emergence of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains has created an urgent need for discovering new anti-tubercular compounds.
This study focuses on identifying inhibitors effective against:
- Replicative phase M.tb
- Non-replicative (drug-tolerant) phase M.tb
The computational models were developed using high-throughput screening datasets and machine learning approaches to accelerate anti-tuberculosis drug discovery.
The datasets were obtained from PubChem BioAssay screens:
- AID-492952 (Replicative phase)
- AID-488890 (Non-replicative phase)
- Total compounds: 2135
- Inhibitors: 1355
- Non-inhibitors: 780
- Total compounds: 2135
- Inhibitors: 1206
- Non-inhibitors: 929
The compounds were processed after removing salts and ion-containing molecules.
Support Vector Machine (SVM)-based classification models were developed using multiple molecular fingerprint classes:
- PubChem fingerprints
- MACCS fingerprints
- EState fingerprints
- SubStructure fingerprints
The models predict whether a compound acts as an inhibitor or non-inhibitor against drug-tolerant M.tb.
- Best MCC: 0.45
- Best model: MACCS fingerprint model
- Accuracy: ~73%
- Best MCC: 0.28
- Best model: Hybrid fingerprint model
- Accuracy: ~64%
The study identified important physicochemical properties associated with anti-tubercular activity.
- Molecular weight
- Polar surface area (PSA)
- Hydrogen bond acceptor count
- Rotatable bond count
- logP
Preferred properties include:
- Molecular weight > 300 Da
- Hydrogen bond acceptor > 5
- Rotatable bond count > 6
- Polar surface area > 88 Ų
Preferred properties include:
- Molecular weight < 380 Da
- Rotatable bond count between 2–4
These rules may assist researchers in designing new anti-tubercular molecules.
Substructure fragment analysis identified important molecular motifs associated with inhibitory activity.
- hetero_N_nonbasic
- heterocyclic
- carboxylic_ester
- hetero_N_basic_no_H
- hetero_O
- ketone
- secondary_mixed_amine
- vinylogous_halide
The following fragments were important in both phases:
- Nitro
- Alkyne
- Enamine
SMART filtering was applied using:
- Abbott ALARM filters
- Glaxo filters
- Pfizer LINT filters
This analysis helped identify undesirable chemical fragments related to toxicity and poor drug-like properties.
Pharmacophore models were generated using first-line and second-line anti-tubercular drugs including:
- Rifampicin
- Ethambutol
- Streptomycin
- Ethionamide
- Cycloserine
- Kanamycin
- Amikacin
These pharmacophore models were used to identify structurally similar compounds within the datasets.
The study introduced a novel feature selection method called:
MCCA was used for selecting informative fingerprints and descriptors for model development.
The method showed performance comparable or superior to frequency-based feature selection methods.
The MDRIPred webserver allows users to:
- Predict inhibitors against drug-tolerant M.tb
- Analyze compounds for replicative and non-replicative activity
- Perform pharmacophore similarity analysis
- Upload molecular structures
- Draw structures using JME editor
Users can submit molecules using:
- JME molecular editor
- MOL/MOL2 file format
- File upload option
The webserver was developed using:
- Linux environment
- CGI-Perl scripts
- SVMlight package
- PaDEL descriptor software
- Pharmagist software
MDRIPred can be used for:
- Anti-tuberculosis drug discovery
- Virtual screening
- Lead optimization
- QSAR studies
- Molecular property analysis
- Pharmacophore screening
- Drug resistance research
The webserver is freely available for academic and research purposes.
Web Server:
http://crdd.osdd.net/oscadd/mdri/
Bioinformatics Centre
CSIR-Institute of Microbial Technology
Chandigarh, India
Email: raghava@iiitd.ac.in
This work is distributed under the
Creative Commons Attribution License
The authors acknowledge support from:
- Council of Scientific and Industrial Research (CSIR)
- Open Source Drug Discovery (OSDD)
- Department of Biotechnology (DBT)
- Government of India
We also acknowledge the PubChem BioAssay resource and the scientific community contributing to tuberculosis drug discovery research.