Using Machine Learning to Predict the Antibacterial Activity of Ruthenium Complexes**

Abstract

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.

Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case

“…12 Specifically, neural networks -the computational model behind deep learning-have shown efficiency in Chemistry [13][14][15] as to classify organic reaction mechanisms, [16][17][18][19][20][21][22][23] to accelerate DFT calculations, 24,25 and to predict molecular properties [26][27][28][29][30][31][32][33] and antibacterial activities. 34 Indeed, despite machine learning methodologies have been applied to achiral nanomaterials, there is no examples including chirality in these structures. 35 It is also worth noting that the approach in the search of new materials with improved properties must meet two important requirements: i) to be able to extrapolate values for the extreme cases, where exceptional materials are, and ii) to be synthetically viable.…”

AttenGpKa: A Universal Predictor of Solvation Acidity Using Graph Neural Network and Molecular Topology

“…Therefore, there is an urgent need for more efficient and accurate methods for predicting p K a . In recent years, the application of machine learning methods in the field of chemistry has made significant advancements, − providing a new paradigm for p K a prediction.…”