Predicting and
preventing the
evolution of antibiotic resistance

How can we outsmart resistance with think-ahead multi-step treatments?

Can antibiotic resistance be reversed?

Can AI-scientists lead groundbreaking discoveries?

 

Using new quantitative experimental approaches alongside clinical data, mathematical modeling, and advanced AI approaches, we study how bacterial pathogens evolve antibiotic resistance during infection. Our goal is to anticipate and counteract this evolution by designing pre-specified, multi-step treatment strategies, planning several moves ahead, much like a game of chess.

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Our Research

AI Science

Closing the loop between AI-driven scientific reasoning, experimentation, and discovery.

Digital Health

From big-data and machine-learning to personalized antibiotic treatment

Ecology, Evolution and Genomics

From genomic evolution to species interactions and community dynamics

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Featured Publications

Non-canonical gene amplifications facilitate adaptive evolution in bacteria
I. Yelin, R. Kishony
Nature Microbiology (2026)

Autonomous LLM-Driven Research — from Data to Human-Verifiable Research Papers
T. Ifargan*, L. Hafner*, M. Kern, O. Alcalay, R. Kishony
NEJM AI (2024)

Minimizing treatment-induced emergence of antibiotic resistance in bacterial infections
M. Stracy, O. Snitser, I. Yelin, Y. Amer, M. Parizade, R. Katz, G. Rimler, T. Wolf, E. Herzel, G. Koren, J. Kuint, B. Foxman, G. Chodick, V. Shalev, R. Kishony
Science (2022), 375(6583), pp.889-894.