Using generative AI, researchers design compounds that can kill drug-resistant bacteria

Good news! ML & AI are coming after the pathogens! Defeat is assured! Say goodbye to antimicrobial resistance! This is only the beginning!

"With help from artificial intelligence, MIT researchers have designed novel antibiotics that can combat two hard-to-treat infections: drug-resistant Neisseria gonorrhoeae and multi-drug-resistant Staphylococcus aureus (MRSA).

Using generative AI algorithms, the research team designed more than 36 million possible compounds and computationally screened them for antimicrobial properties. The top candidates they discovered are structurally distinct from any existing antibiotics, and they appear to work by novel mechanisms that disrupt bacterial cell membranes.

This approach allowed the researchers to generate and evaluate theoretical compounds that have never been seen before — a strategy that they now hope to apply to identify and design compounds with activity against other species of bacteria. ..."

From the highlights and abstract:

"Highlights

• Genetic algorithms and variational autoencoders [VAEs] enable fragment-based and de novo design

• Seven of 24 custom-synthesized compounds show selective antibacterial activity

• Two lead compounds display unique modes of action against N. gonorrhoeae and S. aureus

• Two lead compounds show efficacy against multidrug-resistant strains and in mouse models

Summary

The antimicrobial resistance crisis necessitates structurally distinct antibiotics. While deep learning approaches can identify antibacterial compounds from existing libraries, structural novelty remains limited.

Here, we developed a generative artificial intelligence framework for designing de novo antibiotics through two approaches: 

a fragment-based method to comprehensively screen >107 chemical fragments in silico against Neisseria gonorrhoeae or Staphylococcus aureus, subsequently expanding promising fragments, and

an unconstrained de novo compound generation, each using genetic algorithms and variational autoencoders.

Of 24 synthesized compounds, seven demonstrated selective antibacterial activity. Two lead compounds exhibited bactericidal efficacy against multidrug-resistant isolates with distinct mechanisms of action and reduced bacterial burden in vivo in mouse models of N. gonorrhoeae vaginal infection and methicillin-resistant S. aureus skin infection.

We further validated structural analogs for both compound classes as antibacterial. Our approach enables the generative deep-learning-guided design of de novo antibiotics, providing a platform for mapping uncharted regions of chemical space."

Using generative AI, researchers design compounds that can kill drug-resistant bacteria | MIT News | Massachusetts Institute of Technology "The team used two different AI approaches to design novel antibiotics, including one that showed promise against MRSA."

A generative deep learning approach to de novo antibiotic design (no public access)

Credits: Human Progress weekly newsletter

Graphical abstract