First large lipophilicity dataset for metal complexes sets an ML baseline
- MetalLipoDB curates several thousand literature log P/log D measurements for complexes of 23 metals, including multinuclear and heterometallic species, with measurement conditions and counterions recorded.
- Repeat measurements of identical compounds across studies disagree noticeably, setting a noise floor for modeling.
- Retrospective benchmarks on complexes from unseen articles show moderate errors; calibrating predictions with a few measured members of a series improves the rest. Data, code and a web app are open.
💡 Points Based on the abstract
Inter-study disagreement on identical compounds is the number to watch: differing measurement methods, pH, ionic strength and counterions can swamp any model gain. Splitting by article is the right leakage control, but no prospective synthesis-and-measure test is reported. A team could use the series-calibration trick directly: measure two or three analogues, then rank the rest.
📄 Paper · Journal of Chemical Information and Modeling (ACS) ↗ Beyond Organic Molecules: MetalLipoDB, a Lipophilicity Data Set for Metal Complexes and Machine Learning Benchmarks