1. The stages of drug discovery and development process / A. B. Deore, J. R. Dhumane, R. Wagh, R. Sonawane // Asian Journal of Pharmaceutical Research and Development. – 2019. – Vol. 7, N 6. – P. 62–67. https://doi.org/10.22270/ajprd.v7i6.616
2. Berdigaliyev, N. An overview of drug discovery and development / N. Berdigaliyev, M. Aljofan // Future Medicinal Chemistry. – 2020. – Vol. 12, N 10. – P. 939–947. https://doi.org/10.4155/fmc-2019-0307
3. Roney, M. The importance of in-silico studies in drug discovery / M. Roney, M. F. F. Mohd Aluwi // Intelligent Pharmacy. – 2024. – Vol. 2, N 4. – P. 578–579. https://doi.org/10.1016/j.ipha.2024.01.010
4. In silico methods and tools for drug discovery / B. Shaker, S. Ahmad, J. Lee [et al.] // Computers in Biology and Medicine. – 2021. – Vol. 137. – Art. 104851. https://doi.org/10.1016/j.compbiomed.2021.104851
5. Advances in de novo drug design: from conventional to machine learning methods / V. D. Mouchlis, A. Afantitis, A. Serra [et al.] // International Journal of Molecular Sciences. – 2021. – Vol. 22, N 4. – Art. 1676. https://doi.org/10.3390/ijms22041676
6. Khawbung, J. L. Drug resistant tuberculosis: a review / J. L. Khawbung, D. Nath, S. Chakraborty // Comparative Immunology, Microbiology and Infectious Diseases. – 2021. – Vol. 74. – Art. 101574. https://doi.org/10.1016/j.cimid.2020.101574
7. A deep learning approach to antibiotic discovery / J. M. Stokes, K. Yang, K. Swanson [et al.] // Cell. – 2020. – Vol. 180, N 4. – P. 688–702. https://doi.org/10.1016/j.cell.2020.01.021
8. Identification of new Mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach / T. M. Almeleebia, M. A. Shahrani, M. Y. Alshahrani [et al.] // Molecules. – 2021. – Vol. 26, N 8. – Art. 2326. https://doi.org/10.3390/molecules26082326
9. Machine learning-enabled virtual screening indicates the anti-tuberculosis activity of aldoxorubicin and quarfloxin with verification by molecular docking, molecular dynamics simulations, and biological evaluations / S. Zheng, Ya. Gu, Yu. Gu [et al.] // Briefings in Bioinformatics. – 2024. – Vol. 26, N 1. – Art. bbae696. https://doi.org/10.1093/bib/bbae696
10. Перспективы и препятствия для клинического применения ингибиторов эффлюксных помп Mycobacterium tuberculosis / И. Г. Фелькер, Е. И. Гордеева, Н. В. Ставицкая [и др.] // Биологические мембраны. – 2021. – Т. 38, № 5. – С. 317–339.
11. MmpL3 inhibition as a promising approach to develop novel therapies against tuberculosis: a spotlight on SQ109, clinical studies, and patents literature / M. Imran, M. K. Arora, A. Chaudhary [et al.] // Biomedicines. – 2022. – Vol. 10, N 11. – Art. 2793. https://doi.org/10.3390/biomedicines10112793
12. Specifically targeting Mtb cell-wall and TMM transporter: the development of MmpL3 inhibitors / Q. Luo, H. Duan, H. Yan [et al.] // Current Protein and Peptide Science. – 2021. – Vol. 22, N 4. – P. 290–303. https://doi.org/10.2174/1389203722666210421105733
13. Mycobacterium smegmatis: the vanguard of mycobacterial research / I. L. Sparks, K. M. Derbyshire, W. R. Jr. Jacobs, Ya. S. Morita // Journal of Bacteriology. – 2023. – Vol. 205, N 1. – Art. e00337-22. https://doi.org/10.1128/jb.00337-22
14. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings / C. A. Lipinski, F. Lombardo, B. W. Dominy, P. J. Feeney // Advanced Drug Delivery Reviews. – 2001. – Vol. 46, N 1–3. – P. 3–26. https://doi.org/10.1016/S0169-409X(00)00129-0
15. Sterling, T. ZINC 15 – ligand discovery for everyone / T. Sterling, J. J. Irwin // Journal of Chemical Information and Modeling. – 2015. – Vol. 55, N 11. – P. 2324–2337. https://doi.org/10.1021/acs.jcim.5b00559