cheminformatics, machine learning, drug discovery, opinions
Boston
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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 👀
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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 👀
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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.
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New Practical Cheminformatics post, "Three Papers Demonstrating That Co-folding Still Has a Ways to Go”.
patwalters.github.io/Three-P…
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New Practical Cheminformatics Post, "Useful RDKit Utils - A Mötley Collection of Helpful Routines"
patwalters.github.io/Useful-…
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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…
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Patrick Walters retweeted
Integration Chembl REST API and Claude with MCP #cheminformatics #mcp #ai iwatobipen.wordpress.com/202…
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The latest addition (#33) to the Practical Cheminformatics Tutorials series explores Bayesian optimization of reaction conditions.
github.com/PatWalters/practi…
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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…
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Machine Learning in Drug Discovery Resources page updated for 2025. github.com/PatWalters/resour…
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ChEMBL 35 is out. Happy Holidays!
chembl.blogspot.com/2024/12/…
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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…
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Many of you have seen Gabriele Corso's response to our recent preprint. If you haven't, it's here. lnkd.in/eNvvsRPx
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@prof_ajay_jain's response is here. linkedin.com/pulse/more-resp…
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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
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There’s a new Practical Cheminformatics post, “Some Thoughts on Dataset Splitting,” (with code and a robot cartoon) at practicalcheminformatics.blo…
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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…
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