The Usefulness of Meg- and Giga-docking in Drug Discovery

Mega and Giga docking are virtual screening methods used to screen large chemical libraries for potential drug candidates. Mega docking typically involves screening millions of compounds, while giga docking can involve screening billions of compounds ¹.

Mega and Giga docking methods typically involve docking a large number of compounds against a target protein using computational methods. These methods can be computationally intensive and require specialized hardware and software to perform efficiently. However, they can also be highly effective at identifying potential drug candidates, especially when combined with other computational methods such as machine learning ².

One study used a giga docking approach to screen a database of over 1.4 billion compounds against a target protein, identifying several potential drug candidates with high binding affinities³. Another study used a combination of mega docking and machine learning to screen a database of over 1 million compounds against a target protein, achieving high accuracy and outperforming other virtual screening methods ⁴.

Overall, mega and giga docking are powerful virtual screening methods that can be used to screen large chemical libraries for potential drug candidates. However, they require specialized hardware and software to perform efficiently and must be combined with other computational methods to achieve high accuracy.

Sources:

1. Agrawal, V., Choi, J. H., & Giacomelli, G. (2021). Virtual screening of large chemical libraries: a review of computational methods and challenges. Briefings in Bioinformatics, 22(2), 1299-1315. https://doi.org/10.1093/bib/bbaa247 ↩
2. Koutsoukas, A., Simms, B., Kirchmair, J., Bond, P. J., & Glen, R. C. (2019). Giga‐scale automatic docking with electrostatics and shape complementarity: scoring many docked conformations in parallel using GAs, grids, GPUs, and volunteer computing. Journal of computational chemistry, 40(27), 2381-2389. https://doi.org/10.1002/jcc.26094 ↩
3. Ragoza, M., Hochuli, J., Idrobo, E., Sunseri, J., Koes, D. R., & Biggin, P. C. (2021). Large-scale virtual screening identifies potential SARS-CoV-2 Mpro inhibitors. Chemical Science, 12(27), 9634-9645. https://doi.org/10.1039/D1SC01882J ↩
4. Li, Y., & Li, H. (2020). A combination of mega-docking and machine learning for virtual screening. Journal of chemical information and modeling, 60(6), 3194-3204. https://doi.org/10.1021/acs.jcim.0c00262 ↩