Machine Learning Approach to Anticancer Activity Prediction of Transition-Metal Complexes Based on a Large-Scale Experimental Database

In this work, we developed a straightforward data-driven
approach
to predict the cytotoxicity of metal complexes based entirely on their
(metal + ligands) composition. To this end, we have manually curated
MetalCytoToxDBa comprehensive experimental database comprising
26,500 IC50 values for 7050 metal complexes against 754
cell lines from 1921 articles. Based on these, machine learning models
were created to accurately assign the cytotoxicity class within the
ruthenium and iridium subsets. Moreover, external validation of the
best-performing model on the unseen data was carried out. The possibility
of multimetal predictions was explored, enabling assessment of cytotoxicity
among the complexes of metals, for which experimental data are relatively
scarce. The interpretability and limitations of the developed models
are discussed. Finally, a pipeline for the effective high-throughput
computational screening of ruthenium complexes is proposed. The MetalCytoToxDB
is available online for AI-assisted exploration at https://biometaldb.streamlit.app/.