The oldest arms race
in biological history.
Long before humans existed, bacteria and the compounds that kill them were locked in an evolutionary arms race. Soil fungi evolved antibiotics to compete with bacteria. Bacteria evolved resistance to survive. This war has been running for over a billion years — the molecular machinery of resistance is ancient, deeply conserved, and extraordinarily adaptive.
When Alexander Fleming discovered penicillin in 1928, he handed medicine a weapon developed by nature. What medicine didn't fully appreciate was that nature had also spent a billion years developing countermeasures. Within years of clinical use, resistant strains of Staphylococcus aureus were already appearing. Within decades, resistant strains were common. By the time methicillin-resistant Staphylococcus aureus — MRSA — became widespread in hospitals, the pattern was clear: every antibiotic we deploy selects for the resistance it was designed to defeat.
The pipeline has not kept pace. No genuinely new class of antibiotics has reached the clinic since 1987. Pharmaceutical companies have largely abandoned the field — antibiotics are taken briefly, cure the patient, and generate little revenue compared to drugs for chronic conditions. Meanwhile, the bacteria keep evolving.
"AI models addressing antimicrobial resistance have already identified drug repurposing candidates by virtually screening thousands of compounds — finding that a diabetes drug called Halicin can kill resistant bacteria."
— Center for Security and Emerging Technology, 2025The crisis is not hypothetical. It is already underway. Drug-resistant tuberculosis kills over a million people a year. Carbapenem-resistant Enterobacteriaceae — so-called nightmare bacteria — render last-resort antibiotics useless. The projections for 2050, if trajectories hold, describe a world where routine surgery and cancer chemotherapy become too dangerous to perform, because the infections that follow can no longer be treated.
Four ways bacteria
learn to survive.
Efflux Pumps
Bacteria evolve molecular pumps that actively expel antibiotic molecules before they reach their target — like a cell with a bouncer at the door who ejects the drug as fast as it enters.
Most CommonEnzymatic Degradation
Beta-lactamase enzymes evolved specifically to chemically dismantle penicillin-class antibiotics. Extended-spectrum variants now degrade third-generation cephalosporins as well.
MRSA / ESBLTarget Modification
Bacteria mutate the exact molecular target an antibiotic binds — making the drug's lock useless because the lock has been reshaped while the key hasn't changed.
Vancomycin-RHorizontal Gene Transfer
Bacteria share resistance genes across species through plasmid exchange — meaning a resistance mutation in one species can spread to entirely different bacteria in hours, without reproduction.
Most DangerousA race we've been
losing since 1940.
How artificial intelligence
is fighting back.
The reason AI offers genuine hope against AMR isn't that it's smarter than bacteria. It isn't. But bacteria evolve through blind random mutation and selection — a slow, undirected search through molecular space. AI can search that same space intentionally, guided by structure-activity relationships, resistance mechanisms, and evolutionary trajectories that have taken decades to characterise.
Virtual Compound Screening
Deep learning models screen millions of molecular candidates against bacterial targets in silico — compressing years of wet-lab screening into days and identifying novel scaffolds that human chemists might never have considered.
Resistance Evolution Forecasting
AI models trained on evolutionary data can predict which mutations a pathogen is likely to develop under a given treatment — enabling pre-emptive design of drugs that remain effective even after anticipated resistance emerges.
Drug Repurposing at Scale
Existing approved drugs sometimes kill bacteria through mechanisms unrelated to their primary target. AI mining of molecular databases found Halicin — a diabetes drug — disrupts bacterial membrane potential in a way resistant strains cannot easily counter.
Patient-Specific Resistance Profiling
Rapid whole-genome sequencing of a patient's infecting strain, combined with AI interpretation, can identify the exact resistance genes present and predict which antibiotics will and won't work — in hours rather than days of culture.
Global Resistance Mapping
AI surveillance systems aggregate clinical data across hospitals and regions to track resistance spread in real time — identifying outbreaks earlier and flagging the emergence of novel resistance profiles before they become endemic.
AI-Optimised Bacteriophage Design
Bacteriophages — viruses that kill bacteria — can be engineered to target specific resistant strains. AI accelerates phage-bacteria matching and engineering of phage cocktails that co-evolve with bacterial resistance rather than being outpaced by it.
Resistance as
Bivon logic.
AMR is emergent evolution — Bivology's home territory
Real AMR (Bacteria + Evolution)
- Populations of organisms under selection pressure
- Random mutation as the engine of adaptation
- Resistance as an emergent property of population dynamics
- Horizontal gene transfer as a network effect
- Fitness landscape shaped by antibiotic concentration
- Evolutionary arms race with no endpoint
Bivon AMR (Virtual Populations)
- Digital organism populations under algorithmic pressure
- Rule-based mutation rates as configurable parameters
- Emergent resistance from encoded survival logic
- Virtual gene transfer between Bivon lineages
- Fitness landscapes fully visible and adjustable
- Evolution accelerated to explore all possible outcomes
AMR is perhaps the most natural subject in all of medicine for Bivology. Bacterial resistance evolution is exactly what Bivons do — populations of simple agents, encoding survival rules, adapting under environmental pressure until a new dominant form emerges. The difference is that in Bivology, you can pause the simulation, inspect every individual's internal state, rewind to the moment a resistance mutation was fixed, and ask: what rule change would have prevented this?
Real-world AMR researchers are now asking the same questions virtually. Predictive evolution modelling — simulating which mutations a bacterial population will develop under different antibiotic regimes — is a Bivology-native problem applied to life-or-death medicine. The Bivon that evolves resistance to a simulated antibiotic isn't a toy. It's a proof of concept for the most important biological problem of the 21st century.
More from
this series.
Signal Networks & Disease Tipping Points
How cell-level signaling failures cascade into disease — the same emergence logic that drives resistance evolution, operating at a different biological scale.
Your Virtual Self
Patient-specific digital twins are already being used to predict antibiotic response — matching the right drug to the right resistance profile before treatment begins.
The Tumour Ecosystem
Cancer cells evolving resistance to chemotherapy follows the same evolutionary logic as bacteria evolving antibiotic resistance. The maths is disturbingly similar.
The Last Bivon Standing
A Chronicles story about a Bivon population subjected to escalating selection pressure — told from the perspective of the last susceptible individual watching its lineage disappear.