Aarti Krishnan, Melis N. Anahtar, Jacqueline A. Valeri, Wengong Jin, Nina M. Donghia, Leif Sieben, Andreas Luttens, Yu Zhang, Seyed Majed Modaresi, Andrew Hennes, Jenna Fromer, Parijat Bandyopadhyay, Jonathan C. Chen, Danyal Rehman, Ronak Desai, Paige Edwards, Ryan S. Lach, Marie-Stéphanie Aschtgen, Margaux Gaborieau, Massimiliano Gaetani, Samantha G. Palace, Satotaka Omori, Lutete Khonde, Yurii S. Moroz, Bruce Blough, Chunyang Jin, Edmund Loh, Yonatan H. Grad, Amir Ata Saei, Connor W. Coley, Felix Wong, James J. Collins
The antimicrobial resistance crisis necessitates structurally distinct antibiotics. While deep learning approaches can identify antibacterial compounds from existing libraries, structural novelty remains limited. Here, we developed a generative artificial intelligence framework for designing de novo antibiotics through two approaches: a fragment-based method to comprehensively screen >107 chemical fragments in silico against Neisseria gonorrhoeae or Staphylococcus aureus, subsequently expanding promising fragments, and an unconstrained de novo compound generation, each using genetic algorithms and variational autoencoders. Of 24 synthesized compounds, seven demonstrated selective antibacterial activity. Two lead compounds exhibited bactericidal efficacy against multidrug-resistant isolates with distinct mechanisms of action and reduced bacterial burden in vivo in mouse models of N. gonorrhoeae vaginal infection and methicillin-resistant S. aureus skin infection. We further validated structural analogs for both compound classes as antibacterial. Our approach enables the generative deep-learning-guided design of de novo antibiotics, providing a platform for mapping uncharted regions of chemical space.
October 16, 2025
O'Nolan, D., Sitaula, P., Bellamy, T., Chatterton, L., Amato, K., Todd Ennis, J., Harrison, S., Soukri, M., Blough, B.
Discrete porous coordination cages are attractive as a solution processable material whose porosity is not predicated on a network structure. Here, we leverage the peripheral functionalization of these cage structures to obtain 12 novel, solution processable, porous coordination cages that afford crystalline and amorphous single-phase millimeter-scale monolithic bulk structures (six of each) upon solidification. These structures are based upon prototypal metal–organic polyhedra [Cu24(5-x-isophthalate)24] (where x = NH2, OH), wherein meta-substitution of linker ligands with acyl chloride or isocyanate moieties afforded amide and urethane functional groups, respectively. These porous cage structures were obtainable via direct synthesis between a metal salt and a ligand as well as postsynthetic modification of the cage and formed monoliths following centrifugation and drying of the product. We rationalize their self-assembly as colloidal packing of nanoscale cuboctahedral cages through weak interactions between their hydrophobic alkyl/aromatic surfaces. In general, amorphous solids were obtained via rapid precipitation from the mother liquor upon methanol addition, while crystalline solids could be obtained only following further chloroform and pyridine additions. The structure of the materials is confirmed via gas sorption and spectroscopic methods, while powder X-ray diffraction and transmission electron microscopy are used to determine the nature of these bulk solids.
June 11, 2024