Growth rate and metabolic state of bacteria have been separately shown to affect antibiotic efficacy 1-3 . However, the two are interrelated as bacterial growth inherently imposes a metabolic burden 4 ; thus, determining individual contributions from each is challenging 5,6 . Indeed, faster growth is often correlated with increased antibiotic efficacy 7,8 ; however, the concurrent role of metabolism in that relationship has not been well characterized. As a result, a clear understanding of the interdependence between growth and metabolism, and their implications for antibiotic Reprints and permissions information is available at www.nature.com/reprints.

Efficient identification of drug mechanisms of action remains a challenge. Computational docking approaches have been widely used to predict drug binding targets; yet, such approaches depend on existing protein structures, and accurate structural predictions have only recently become available from AlphaFold2. Here, we combine AlphaFold2 with molecular docking simulations to predict protein-ligand interactions between 296 proteins spanning Escherichia coli's essential proteome, and 218 active antibacterial compounds and 100 inactive compounds, respectively, pointing to widespread compound and protein promiscuity. We benchmark model performance by measuring enzymatic activity for 12 essential proteins treated with each antibacterial compound. We confirm extensive promiscuity, but find that the average area under the receiver operating characteristic curve (auROC) is 0.48, indicating weak model performance. We demonstrate that rescoring of docking poses using machine learning-based approaches improves model performance, resulting in average auROCs as large as 0.63, and that ensembles of rescoring functions improve prediction accuracy and the ratio of true-positive rate to false-positive rate. This work indicates that advances in modeling protein-ligand interactions, particularly using machine learning-based approaches, are needed to better harness AlphaFold2 for drug discovery.

Highlights d Antibiotics are strongly or weakly dependent on metabolism (SDM or WDM) d Combinations of SDM and WDM antibiotics sterilize bacteria, while dose-sparing d SDM and WDM drug interactions are undetectable in growthinhibition assays