Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Editorial
. 2021 Mar 29;7(6):FSO702.
doi: 10.2144/fsoa-2021-0030.

State-of-the-art of artificial intelligence in medicinal chemistry

Jürgen Bajorath  1
Affiliations
Editorial

State-of-the-art of artificial intelligence in medicinal chemistry

Jürgen Bajorath. Future Sci OA. .
No abstract available

Keywords: artificial intelligence; big data; chemical reaction modeling; compound design; machine learning; medicinal chemistry; property prediction; small data.

PubMed Disclaimer

Conflict of interest statement

Financial & competing interests disclosure The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties. No writing assistance was utilized in the production of this manuscript.

References

    1. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5), 206–215 (2019). - PMC - PubMed

    2. •• To-the-point discussion of black box, explainable and interpretable machine learning.

    1. Struble TJ, Alvarez JC, Brown SP et al. Current and future roles of artificial intelligence in medicinal chemistry synthesis. J. Med. Chem. 63(16), 8667–8682 (2020). - PMC - PubMed

    2. • Informative perspective of artificial intelligence approaches aiding in chemical synthesis.

    1. Chen H, Engkvist O, Wang Y, Olivecrona M, Blaschke T. The rise of deep learning in drug discovery. Drug Discov. Today 23(6), 1241–1250 (2018). - PubMed
    1. Bort W, Baskin II, Gimadiev T et al. Discovery of novel chemical reactions by deep generative recurrent neural network. Sci. Rep. 11(1), 1–15 (2021). - PMC - PubMed
    1. Schwaller P, Probst D, Vaucher AC et al. Mapping the space of chemical reactions using attention-based neural networks. Nat. Mach. Intell. (2021). 10.1038/s42256-020-00284-w - DOI

Publication types

LinkOut - more resources