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🗞️ News - July 30, 2025

AI Identifies New Antibiotic Candidates from Venom

AI identifies potential antibiotics from snake and spider venom, offering hope against antibiotic resistance. 🐍🕷️💊

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AI Identifies New Antibiotic Candidates from Venom

Overview

Recent research has revealed that snake, scorpion, and spider venom, typically known for their toxic properties, may hold the key to combating antibiotic resistance, which is responsible for over one million deaths globally each year.

Research Findings
  • A study published in Nature Communications details how researchers at the University of Pennsylvania utilized a deep-learning system named APEX to analyze a database of over 40 million venom-encoded peptides (VEPs).
  • In just a few hours, the AI identified 386 compounds that exhibit characteristics of next-generation antibiotics.
  • According to senior author César de la Fuente, PhD, “Venoms are evolutionary masterpieces, yet their antimicrobial potential has barely been explored.”
Laboratory Testing

From the AI-generated shortlist, the team synthesized 58 venom peptides for laboratory evaluation. The results showed that:

  • 53 peptides were effective against drug-resistant bacteria, including Escherichia coli and Staphylococcus aureus, at doses safe for human red blood cells.
  • The study represents one of the most thorough investigations into venom-derived antibiotics to date.
Future Directions

The research team is now focused on enhancing the most promising peptide candidates through medicinal chemistry modifications. This could lead to the development of new antibiotics capable of addressing the growing issue of antibiotic resistance.

Funding and Support

This research received support from various organizations, including:

  • Procter & Gamble Company
  • United Therapeutics
  • BBRF Young Investigator Grant
  • Nemirovsky Prize
  • Penn Health-Tech Accelerator Award
  • Defense Threat Reduction Agency grants
  • Dean’s Innovation Fund from the Perelman School of Medicine at the University of Pennsylvania

For further details, refer to the study: Guan C, Torres MDT, Li S, de la Fuente-Nunez C. Computational exploration of global venoms for antimicrobial discovery with Venomics artificial intelligence. Nat Commun. 2025 Jul 12;16(1):6446.

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