Machine learning for medicinal chemistry
| Platform |  | Pricing | Only free issues | Publishes | Monthly |
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We now know what it will take
Synopsis: In this post and a preprint we show that a model trained with large amounts of protein-ligand interaction data collected using DNA-encoded chemical libraries (DELs) can compete with the current state-of-the-art. We generate additi...
AI for chemistry in 2025 is like AI for images in 2010
Synopsis: In this post and a preprint we show that a simple model can identify which chemist made a given molecule from structure alone, and that you can use authorship to make pretty good activity predictions without any knowledge of chemi...
Taking away the protein cheat sheet
Synopsis: By identifying where protein-ligand interaction models pay attention, we see ours trained on less data are likely memorizing proteins rather than wrestling with the structural contributions of individual amino acids. To discourage...
Hit expansion at scale with Hermes
TL;DR
Hermes is a compact, fast binding prediction model trained entirely on in-house DEL data collected here at Leash. Building on Hermes, we created Artemis, a hit expansion tool that applies chemist-inspired SMARTS transformations and...
Data contamination is all random forest needs
The Fortune-Teller. Georges de La Tour, c. 1630.
Earlier this month we described Hermes, a simple model trained on massive amounts of physical measurements that we’ve generated here at Leash: Hermes achieves state-of-the-art performance on...
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Authors
The writers behind this newsletter.
Life science and machine learning guy. Cofounder of Leash, ex-Recursion.
Data Scientist @ Leash Bio
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