The safe storage of blood is of fundamental importance to health care systems all over the world. Currently, plastic bags are used for the collection and storage of donated blood and are typically made of poly­(vinyl chloride) (PVC) plasticized with di-2-ethylhexyl phthalate (DEHP). DEHP is known to migrate into packed red blood cells (RBC) and has been found to extend their shelf life. It has been speculated that DEHP incorporates itself into the RBC membrane and alters membrane properties, thereby reducing susceptibility to hemolysis and morphological deterioration. Here, we used high-resolution X-ray diffraction and molecular dynamics (MD) simulations to study the interaction between DEHP and model POPC lipid membranes at high (9 mol %) and low (1 mol %) concentrations of DEHP. At both concentrations, DEHP was found to spontaneously partition into the bilayer. At high concentrations, DEHP molecules were found to aggregate in the aqueous phase before inserting as clusters into the membrane. The presence of DEHP in the bilayers resulted in subtle, yet statistically significant, alterations in several membrane properties in both the X-ray diffraction experiments and MD simulations. DEHP led to (1) an increase of membrane width and (2) an increase in the area per lipid. It was also found to (3) increase the deuterium order parameter, however, (4) decrease membrane orientation, indicating the formation of thicker, stiffer membranes with increased local curvature. The observed effects of DEHP on lipid bilayers may help to better understand its effect on RBC membranes in increasing the longevity of stored blood by improving membrane stability.

As the global burden of antibiotic resistance continues to grow, creative approaches to antibiotic discovery are needed to accelerate the development of novel medicines. A rapidly progressing computational revolution—artificial intelligence—offers an optimistic path forward due to its ability to alleviate bottlenecks in the antibiotic discovery pipeline. In this review, we discuss how advancements in artificial intelligence are reinvigorating the adoption of past antibiotic discovery models—namely natural product exploration and small molecule screening. We then explore the application of contemporary machine learning approaches to emerging areas of antibiotic discovery, including antibacterial systems biology, drug combination development, antimicrobial peptide discovery, and mechanism of action prediction. Lastly, we propose a call to action for open access of high‐quality screening datasets and interdisciplinary collaboration to accelerate the rate at which machine learning models can be trained and new antibiotic drugs can be developed.