By Gabe Sanders PhD, NSCA-CSCS
What if finding life-saving medicines could happen in hours instead of years?
Thanks to breakthroughs in artificial intelligence (AI), this idea is becoming reality. At Stanford Medicine, scientists have created an AI model called SyntheMol, designed to speed up drug discovery and improve precision in fighting infections.
Recently, SyntheMol tackled a major health challenge: identifying drugs to combat a dangerous antibiotic-resistant bacteria that poses serious risks in hospitals around the world.
SyntheMol speeds up drug discovery
Traditionally, creating new drugs is like searching for a needle in a haystack. Scientists test millions of chemical combinations to find the ones that might work, which can take years and cost millions of dollars. But SyntheMol speeds up this process by analyzing vast chemical data in just hours, suggesting the best candidates to fight infections.
In one case, SyntheMol reviewed thousands of chemical possibilities and identified around 70 promising options, six of which turned out to be effective against the bacteria. This entire process took under nine hours, a fraction of the time traditional methods would need.
AI can be a powerful ally
AI is bringing a new spark of hope for tackling some of the most challenging health issues, from cancer and Alzheimer’s to antibiotic-resistant infections. While many are understandably cautious about AI, and rightly so, this technology has the potential to assist, not replace, the expertise of doctors and researchers.
In medicine, AI can be a powerful ally, accelerating the search for new drugs and making once-unthinkable treatments possible. By quickly pinpointing promising options, AI could help move treatments from the lab to patients faster than ever before. With AI as a trusted partner, the future of medicine could mean more effective, timely care for all.
Article’s Source
Swanson, Kyle, et al. “Generative AI for designing and validating easily synthesizable and structurally novel antibiotics.” Nature Machine Intelligence 6.3 (2024): 338-353.
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