RESEARCH TRIANGLE PARK – IBM scientists have utilized artificial intelligence to help speed up development of molecules for potential use in new “novel antibiotics” that are needed as the spread of antibiotic resistance grows and the need for new drugs increases.
In a blog post and a paper published in Nature Biomedical Engineering, the IBM team said the system would help pace the way to “accelerated discovery.”
“[O]ur IBM Research team has developed an AI system that can help speed up the design of molecules for novel antibiotics. And it works,” wrote Aleksandra Mojsilovic and Payel Das in the blog.
Noting the rise of resistance to antibiotics, the two said the threat “is no joke. It’s a huge threat to human health — even more so during the raging pandemic. We need new antibiotics, and we need them fast.”
AI could help provide part of a better solution.
The paper is titled “Accelerating Antimicrobial Discovery with Controllable Deep Generative Models and Molecular Dynamics.”
“[W]e outline how we used it to create two new non-toxic antimicrobial peptides (AMPs) with strong broad-spectrum potency. Peptides are small molecules — they are short strings of amino acids, the building blocks of proteins. Our approach outperforms other leading de novo AMP design methods by nearly 10 percent,” the two scientists wrote.
The IBM scientists warned that “very few new antibiotics are being developed to replace those that no longer work. That’s because drug design is an extremely difficult and lengthy process — there are more possible chemical combinations of a new molecule than there are atoms in the Universe.”
“We want to help,” they wrote.
In the papers’ abstract, the research team notes progress was madein less than seven weeks:
“The de novo [from the beginning] design of antimicrobial therapeutics involves the exploration of a vast chemical repertoire to find compounds with broad-spectrum potency and low toxicity. Here, we report an efficient computational method for the generation of antimicrobials with desired attributes. The method leverages guidance from classifiers trained on an informative latent space of molecules modelled using a deep generative autoencoder, and screens the generated molecules using deep-learning classifiers as well as physicochemical features derived from high-throughput molecular dynamics simulations. Within 48 days, we identified, synthesized and experimentally tested 20 candidate antimicrobial peptides, of which two displayed high potency …”
