Until now, the natural world has supplied us with antibiotics. Bacteria, however, are increasingly resistant to these drugs. The next generation of antibiotics will likely come not from nature but from machine learning, artificial intelligence (AI) algorithms, and computer-based discovery. Working at the forefront of this development, I seek to harness computational power to find molecules with antibacterial activity. I use AI to determine features contributing to this activity and train computers to find— or design— candidate molecules and tweak their structures virtually. Experimentation is reserved for validating computer predictions, saving time, labor, and expense. With autonomous machine-based molecular discovery, I explore proteins and peptides as engineering scaffolds. My computerized approaches diversify proteins, such as host defense peptides (HDPs), beyond their natural variation. To increase their antimicrobial properties, we trained a computer to execute a fitness function that selects for structures that interact with bacterial membranes, thereby converting several HDPs into the first artificial antimicrobials that kill bacteria. By investigating these exciting possibilities, I aim to build machine-made antibiotics to combat infectious diseases and develop clinical applications for autonomously generated synthetic molecules. Computer-made drugs may help to replenish our arsenal of effective drugs and outpace the evolution of antibiotic resistance.

Towards computer-made antibiotics

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.