It will work best for the robot that made the data as we have more controlled conditions than the mess we have in our current available data , but it can probably be transfered to others with a few select experiments and adjustments. Not sure about QM, I'm not a big expert there.

We need robitics before we can standardize the conditions enough to really make the ML shine. On the other hand we need the predictions and ML to make the robotics work.

Tillykke😉

Ah, I dont think Ill be in DK that date...

Sadly, many coutries didn't even bother to care about the scientists already living there. US scientists are not special, they just had funding.

Ja, underlige regler er der mange af. Men hele forskningsområdet har jo været udsultet over mange årtier, så det er lidt provokerende at der nu pludseligt skal gøres ekstra for specielle nationaliteter. Jeg selv endte med at udvandre, og det er nok ikke DK jeg flytter til hvis jeg flytter land igen.

Hvad med at tiltrække forskere fra Danmark?

8/ 📣 How do you use applicability domain estimation in chemML workflows?
Are these features helping your model validation or deployment? Let us know — and feel free to contribute or suggest more!
#cheminformatics #MachineLearning #OpenSource

7/ 🙌 Huge thanks to @asiomchen, @Productivista, @mieczyslaw — and of course Olivier — for pushing the project forward!
We’re excited to see how the community uses these new tools. 👨‍🔬👩‍🔬

6/ 2️⃣ Improved Custom Fingerprinting
We've made it easier to create and use your own molecular fingerprints.
3️⃣ Cleaner Code & UX Polish
• Typos fixed (yes, CheckSmilesSanitazion is now spelled right)
• Better subclass handling
• New hybrid logo for both light and dark background 🎨

5/ 🔬 Try a full example:
👉 scikit-mol.readthedo...
Whether you're validating models or deciding what predictions to trust — AD is now built-in.

4/ Includes:
• kNN, Leverage, and more
• Unified interfaces:
.predict() → in/out classification
.transform() → raw scores
.score_transform() → soft scores (0–1)
• Support for threshold fitting from validation sets
🧪

3/ 1️⃣ Applicability Domain Estimators
Thanks to a generous code donation from Olivier J. M. Béquignon's MLChemAD, we’ve added a suite of AD estimators—standardized and integrated into the Scikit-Mol framework. 🙏

2/ 🔬 What is Scikit-Mol?
An open-source library connecting #RDKit featurization with #Scikit-Learn workflows. It offers plug-and-play descriptors and fingerprints for ML in life sciences.
📦 github.com/EBjerrum/...

I remember I installed slackware Linux on my 486 laptop with 4 mb of ram using my brothers zipdrive. Had to hack the modelines to get X to work. The zipdisks had more storage than my harddrive! Nowadays its boringly easy.

C:/>

Who was he mocking?

Whats the timings for both of them?

Yes, it seems like an unnessecary complication.