cheminformatics, machine learning, drug discovery, opinions
Boston
Joined June 2009
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A great opportunity to showcase your marimo skills and compete for some solid prizes. Looking forward to seeing what the community builds.
33 days to win a mac mini.
the challenge: pick a cheminformatics dataset and build a marimo notebook that answers a question you want the answer to.
we're judging based on creativity, not on model accuracy. co-hosted with @wpwalters.
we'll see you in our submissions 👀
Excited to team up with @marimo to explore new relationships between chemical structures and biological data.
Bring your creativity, push the boundaries of reactive notebooks, and compete for prizes! Details below 👇
We're running a cheminformatics notebook competition with @wpwalters and OpenADMET. Take a dataset & bring cheminformatics to life.
Mac Mini + $2.5K in prizes, deadline Oct 4 👀
An interesting piece about Axiom Bio's work combining cell painting and machine learning to predict toxicity
For a decade, techbio optimized the drug discovery "paper mill": more molecules, faster. But the hard part - knowing which ones will work safely in humans - hardly moved at all.
Late last year, I spent time with Axiom Bio, a company built around the idea that the real bottleneck is predicting clinical activity in molecules, *not* merely discovering them.
New Practical Cheminformatics post, "Three Papers Demonstrating That Co-folding Still Has a Ways to Go”.
patwalters.github.io/Three-P…
New Practical Cheminformatics Post, "Useful RDKit Utils - A Mötley Collection of Helpful Routines"
patwalters.github.io/Useful-…
The latest Practical Cheminformatics post, “The Trouble With Tautomers,” emerged from a discussion about the impact of tautomers on machine learning model predictions.
patwalters.github.io/The-Tro…
Patrick Walters retweeted
Integration Chembl REST API and Claude with MCP #cheminformatics #mcp #ai iwatobipen.wordpress.com/202…
The latest addition (#33) to the Practical Cheminformatics Tutorials series explores Bayesian optimization of reaction conditions.
github.com/PatWalters/practi…
In a new Practical Cheminformatics post titled "Even More Thoughts on ML Method Comparisons," I share several plots that I find valuable for comparing machine learning methods.
practicalcheminformatics.blo…
Machine Learning in Drug Discovery Resources page updated for 2025. github.com/PatWalters/resour…
Excellent new paper (with code) by my former colleagues Steven Kearnes and Patrick Riley describing a procedure for associating confidence levels with regression model predictions in drug discovery. pubs.acs.org/doi/10.1021/acs…
Many of you have seen Gabriele Corso's response to our recent preprint. If you haven't, it's here. lnkd.in/eNvvsRPx
@prof_ajay_jain's response is here. linkedin.com/pulse/more-resp…
I'm thrilled to announce a new preprint describing collaborative work with @prof_ajay_jain and Ann Cleves Jain, "Deep-Learning Based Docking Methods: Fair Comparisons to Conventional Docking Workflows".
arxiv.org/abs/2412.02889
There’s a new Practical Cheminformatics post, “Some Thoughts on Dataset Splitting,” (with code and a robot cartoon) at practicalcheminformatics.blo…
Patrick Walters retweeted
Introducing our first proposed set of guidelines for method comparison in small molecule property prediction!
Crafted by the Small Molecule Steering Committee, the pre-print introduces statistically rigorous, domain-appropriate comparison protocols for small molecule predictive modelling to help ensure replicability and practical impact. What do you think of our proposal? 🧵👇
Pre-print: chemrxiv.org/engage/chemrxiv…
Leave your feedback and help us redefine how method comparison is done: github.com/polaris-hub/polar…
