Beyond Organic Molecules: MetalLipoDB, a Lipophilicity Dataset for Metal Complexes and Machine Learning Benchmarks
Year: 2026
Type: preprint
Abstract: Lipophilicity governs the uptake, distribution and toxicity of drug candidates, yet the experimental logP datasets used to build predictive models contain almost exclusively organic molecules. Here we present MetalLipoDB, a manually curated dataset of 5158 experimental logP and logD values of complexes of 23 metals from 1192 articles, including 556 entries for multinuclear and heterometallic complexes, annotated with the measurement conditions and counterions. The dataset quantifies the inter-study reproducibility of these measurements (0.56 log units) and serves as a benchmark for machine learning, with a mean absolute error of 0.72–0.75 for complexes from unseen articles. For articles with at least four measurements, anchoring the predictions to three measured complexes of the series reduced the error on the remaining complexes from 0.73 to 0.51. MetalLipoDB, the code and an interactive web application (https://biometaldb.streamlit.app/) are openly available to support the design of metal-based drugs.
Source: ChemRxiv
Language: English
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7220863078