Integrated Computational and Experimental Framework for Inverse Screening of Candidate Antibacterial Nanomedicine.

Nanomedicine is promising for disease prevention and treatment, but there are still many challenges that hinder its rapid development. A major challenge is to efficiently seek candidates with the desired therapeutic functions from tremendously available materials. Here, we report an integrated computational and experimental framework to seek alloy nanoparticles from the Materials Project library for antibacterial applications, aiming to learn the inverse screening concept from traditional medicine for nanomedicine. Because strong peroxidase-like catalytic activity and weak toxicity to normal cells are the desired material properties for antibacterial usage, computational screening implementing theoretical prediction models of catalytic activity and cytotoxicity is first conducted to select the candidates. Then, experimental screening based on scanning probe block copolymer lithography is used to verify and refine the computational screening results. Finally, the best candidate AuCu is synthesized in solution and its antibacterial performance over other nanoparticles against and . is experimentally confirmed. The results show the power of inverse screening in accelerating the research and development of antibacterial nanomedicine, which may inspire similar strategies for other nanomedicines in the future.