Rising antimicrobial resistance (AMR) and lack of innovation in the antibiotic pipeline necessitate novel approaches to discovering new drugs. Metal complexes have proven to be promising antimicrobial compounds, but the number of studied compounds is still low compared to the millions of organic molecules investigated so far. Lately, machine learning (ML) has emerged as a valuable tool for guiding the design of small organic molecules, potentially even in low-data scenarios. For the first time, we extend the application of ML to the discovery of metal-based medicines. Utilising 288 modularly synthesized ruthenium arene Schiff-base complexes and their antibacterial properties, a series of ML models were trained. The models perform well and are used to predict the activity of 54 new compounds. These displayed a 5.7x higher hit-rate (53.7%) against methicillin-resistant Staphylococcus aureus (MRSA) compared to the original library (9.4%), demonstrating that ML can be applied to improve the success-rates in the search of new metalloantibiotics. This work paves the way for more ambitious applications of ML in the field of metal-based drug discovery.
Contains Machine Learning Evaluation. Predicted Building Block Correlations with SlogP, TPSA and MW, Feature Importance from SVM, Pairwise Tanimoto similarities of building blocks, MIC of active complexes, conversion efficiency of the new library and Growth inhibition of new building blocks against MRSA, HPLC and NMR data on resynthesized complexes and LC-MS traces of new library