An interesting piece about Axiom Bio's work combining cell painting and machine learning to predict toxicity

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…

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…

ChEMBL 35 is out. Happy Holidays! chembl.blogspot.com/2024/12/…

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…

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…

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…

Excited to introduce the first steering committee (SC) from Polaris! This group of industry experts is focused on small-molecule, predictive modelling tasks and is collaborating to develop guidelines for benchmarking best practices. At Polaris, our mission is to bring innovators and practitioners closer together to develop methods that matter. The first SC publication in @NatMachIntell outlines the common challenges in benchmarking and serves as a call to action for the community emphasizing the importance of cross-industry collaboration. Next, we’ll be releasing a pre-print on method comparison, providing guidance on comparison protocols and domain-appropriate performance metrics to ensure reproducibility in real-world settings. (1/6) 🧵 👇 Read the correspondence letter: rdcu.be/dVZHz Read the announcement: polarishub.io/blog/introduci…

There’s a new Practical Cheminformatics blog post, “Silly Things Large Language Models Do With Molecules.” In this post, I explain why general-purpose LLMs aren’t appropriate for analog generation and suggest better alternatives. practicalcheminformatics.blo…

Thanks to everyone who attended the AI in Drug Discovery Workshop at the RSC 7th Symposium on AI in Chemistry.  We’ve made the materials (slides, Jupyter notebooks, resource list) from the three hour workshop available on GitHub.  #AIChem24 github.com/volkamerlab/ai_in…