References
- Winkler DA. Use of artificial intelligence and machine learning for discovery of drugs for neglected tropical diseases. Front Chem. 2021;9:614073.
- Ragno R, Marshall GR, Di Santo R, et al. Antimycobacterial pyrroles: synthesis, anti-mycobacterium tuberculosis activity and QSAR studies. Bioorg Med Chem. 2000 Jun;8(6):1423–1432.
- Kumar A, Siddiqi MI. Receptor based 3D-QSAR to identify putative binders of mycobacterium tuberculosis enoyl acyl carrier protein reductase. J Mol Model. 2010 May;16(5):877–893.
- Maganti L, Ghoshal N, Consortium O. 3D-QSAR studies and shape based virtual screening for identification of novel hits to inhibit MbtA in mycobacterium tuberculosis. J Biomol Struct Dyn. 2015 Feb 1;33(2):344–364.
- Ekins S, Godbole AA, and Keri G, et al. Machine learning and docking models for mycobacterium tuberculosis topoisomerase I. Tuberculosis. 2017 Mar;103:52–60.
- Fujita T, and Winkler DA. Understanding the roles of the “two QSARs.” J Chem Inf Model. 2016 Feb22;56(2):269–274.
- Le TC, Winkler DA. A bright future for evolutionary methods in drug design. ChemMedChem. 2015 Aug;10(8):1296–1300.