AI finds hundreds of potential antibiotics in snake and spider venom
Gaby Clark
scientific editor
Andrew Zinin
lead editor
Snake, scorpion, and spider venom are most frequently associated with poisonous bites, but with the help of artificial intelligence, they might be able to help fight antibiotic resistance, which than one million deaths worldwide each year.
In a published in Nature Communications, researchers at the University of Pennsylvania used a deep-learning system called APEX to sift through a database of more than 40 million venom encrypted peptides (VEPs), tiny proteins evolved by animals for attack or as a defense mechanism. In a matter of hours, the algorithm flagged 386 compounds with the molecular hallmarks of next-generation antibiotics.
"Venoms are evolutionary masterpieces, yet their antimicrobial potential has barely been explored," said senior author César de la Fuente, Ph.D., a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry. "APEX lets us scan an immense chemical space in just hours and identify peptides with exceptional potential to fight the world's most stubborn pathogens."
Combining emerging tech with established methods
From the AI-selected shortlist, the team synthesized 58 venom peptides for laboratory testing. 53 killed drug-resistant bacteria—including Escherichia coli and Staphylococcus aureus—at doses that were harmless to human red blood cells.
"By pairing computational triage with traditional lab experimentation, we delivered one of the most comprehensive investigations of venom derived antibiotics to date," added co-author Marcelo Torres, Ph.D., a research associate at Penn. Changge Guan, Ph.D., a postdoctoral researcher in the De la Fuente Lab and co-author, noted that the platform mapped more than 2,000 entirely new antibacterial motifs—short, specific sequences of amino acids within a protein or peptide responsible for their ability to kill or inhibit bacterial growth.
The team is now taking the top peptide candidates which could lead to new antibiotics and improving them through medicinal-chemistry tweaks.
More information: Changge Guan et al, Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence, Nature Communications (2025).
Journal information: Nature Communications
