The chemical structure must fullfil the following requirements:
• Structure should be uncharged or charges should be balanced
• Only single, double and triple bonds are allowed
• Structure should contain at least 3 carbon atoms
• Structure should contain only one component, single atoms are not considered
• Absolute molecular weight should exceed 1250
• Discovery of novel antibacterial agents is important ongoning task and this process benefits from using (Q)SAR methods, which are based on the extensively validated predictive methods and experimental data accumulated in the field.
General information:
Data on antibacterial action of chemical compounds are well represented in public domain. ChEMBL database [2], for example, contains records on activity of chemical compounds against more than 1386 bacteria. We extracted bioactivity records on minimum inhibitory concentrations (MICs) of chemical compounds from ChEMBL_24 and prepared them as follows:
• Chemical data were prepared according to the good (Q)SAR practice [3].
• Biological data were reviewed to exclude the unreliable data points and to specify the records, related to the resistant microorganisms.
In general, pipeline for the data preparation was similar to those and described in publications [4, 5].
The training set containing structures of 41,065 chemical compounds and data on their antibacterial activities was prepared. All molecules with MIC < 10000 nM were considered as “actives”. Using this set, we trained PASS [6] to classify drug-like molecule as “actives” and “inactives” against 353 bacteria, including resistant ones. The average accuracy of prediction assessed as IAP (corresponds numerically to the ROC AUC) in Leave-One-Out Cross-Validation equals 0.93 (for particular biological activities IAP was in range from 0.75 to 1.00).
Update from january 2026: RDKit.JS is now used for the intial chemical structure processing [7] instead of the MarvinJS (https://chemaxon.com/). Also, the code of antiBac pages is available via GitHub:
https://github.com/pavelVPo/IBMC_LSFBD_webApp_antibac
References:
1. Brown, E. D., Wright, G. D. (2016), Antibacterial drug discovery in the resistance era. Nature, 529(7586), 336.
2. Gaulton, A., et al. (2016), The ChEMBL database in 2017. Nucleic acids research, 45(D1), D945-D954.
3. Fourches, D., et al.. (2015), Curation of chemogenomics data. Nature chemical biology, 11(8), 535.
4. Pogodin, P. V., et al. (2015), PASS Targets: Ligand-based multi-target computational system based on a public data and naïve Bayes approach. SAR and QSAR in Environmental Research, 26(10), 783-793.
5. Pogodin, P., et al. (2018), How to Achieve Better Results Using PASS-Based Virtual Screening: Case Study for Kinase Inhibitors. Frontiers in chemistry, 6, 133.
6. Filimonov, D. A., et al. (2014). Prediction of the biological activity spectra of organic compounds using the PASS online web resource. Chemistry of Heterocyclic Compounds, 50(3), 444-457.
7. Landrum, G. (2013). Rdkit documentation. Release, 1(1-79), 4.