layout | title | date | modified_date | tags | doi | image |
---|---|---|---|---|---|---|
post |
Artificial intelligence for natural product drug discovery |
2023-09-24 |
2024-03-18 |
cheminf natprod doi:10.1038/s41573-023-00774-7 doi:10.7554/eLife.70780 |
10.59350/dtyms-yt012 |
/assets/images/ai.png |
Two weeks ago the write up of a week-long scientific discussions around artificial intelligence for natural product drug discovery in Leiden at the Lorentz Center got published (doi:10.1038/s41573-023-00774-7, free PDF).
Sadly, the meetings was still during the (partial) lockdown, and I think my contribution could have been more extensive. But I am happy I got to pitch the idea of using Wikidata in this area too, taking advantage of the work done by the LOTUS (doi:10.7554/eLife.70780) team earlier.
And this is key to me: you cannot do statistics, chemometrics, machine learning, or artificial intelligence without good quality linked data. Happy reading!