{"id":71536,"date":"2026-02-16T11:48:29","date_gmt":"2026-02-16T11:48:29","guid":{"rendered":"https:\/\/youzum.net\/the-scientist-using-ai-to-hunt-for-antibiotics-just-about-everywhere\/"},"modified":"2026-02-16T11:48:29","modified_gmt":"2026-02-16T11:48:29","slug":"the-scientist-using-ai-to-hunt-for-antibiotics-just-about-everywhere","status":"publish","type":"post","link":"https:\/\/youzum.net\/th\/the-scientist-using-ai-to-hunt-for-antibiotics-just-about-everywhere\/","title":{"rendered":"The scientist using AI to hunt for antibiotics just about everywhere"},"content":{"rendered":"

When he was just a teenager trying to decide what to do with his life, C\u00e9sar de la Fuente compiled a list of the world\u2019s biggest problems. He ranked them inversely by how much money governments were spending to solve them. Antimicrobial resistance topped the list.\u00a0<\/p>\n

Twenty years on, the problem has not gone away. If anything, it\u2019s gotten worse. Infections caused by bacteria, fungi, and viruses that have evolved ways to evade treatments are now associated with more than 4 million deaths per year, and a recent analysis, published in the Lancet<\/em>, predicts that number could surge past 8 million by 2050. In a July 2025 essay<\/a> in Physical Review Letters<\/em>, de la Fuente, now a bioengineer and computational biologist, and synthetic biologist James Collins warned of a looming \u201cpost\u00adantibiotic\u201d era in which infections from drug-resistant strains of common bacteria like Escherichia coli <\/em>\u0e2b\u0e23\u0e37\u0e2d Staphylococcus aureus<\/em>, which can often still be treated by our current arsenal of medications, become fatal. \u201cThe antibiotic discovery pipeline remains perilously thin,\u201d they wrote, \u201cimpeded by high development costs, lengthy timelines, and low returns on investment.\u201d<\/p>\n

But de la Fuente is using artificial intelligence to bring about a different future. His team at the University of Pennsylvania is training AI tools to search genomes far and deep for peptides with antibiotic properties. His vision is to assemble those peptides\u2014molecules made of up to 50 amino acids linked together\u2014into various configurations, including some never seen in nature. The results, he hopes, could defend the body against microbes that withstand traditional treatments.\u00a0<\/p>\n

His quest has unearthed promising candidates in unexpected places. In August 2025 his team, which includes 16 scientists in Penn\u2019s Machine Biology Group, described peptides<\/a> hiding in the genetic code of ancient single-celled organisms called archaea. Before that, they\u2019d excavated a list of candidates from the venom of snakes, wasps, and spiders. And in an ongoing project de la Fuente calls \u201cmolecular de-\u00adextinction,\u201d he and his collaborators have been scanning published genetic sequences of extinct species for potentially functional molecules. Those species include hominids like Neanderthals and Denisovans and charismatic megafauna like woolly mammoths, as well as ancient zebras and penguins. In the history of life on Earth, de la Fuente reasons, maybe some organism evolved an antimicrobial defense that could be helpful today. Those long-gone codes have given rise to resurrected compounds with names like \u00admammuthusin-2 (from woolly mammoth DNA), mylodonin-2 (from the giant sloth), and hydrodamin-1 (from the ancient sea cow). Over the last few years, this molecular binge has enabled de la Fuente to amass a library of more than a million genetic recipes.<\/p>\n

At 40 years old, de la Fuente has also collected a trophy case of awards from the American Society for Microbiology, the American Chemical Society, and other organizations. (In 2019, this magazine named him<\/a> one of \u201c35 Innovators Under 35\u201d for bringing computational approaches to antibiotic discovery.) He\u2019s widely recognized as a leader in the effort to harness AI for real-world problems. \u201cHe\u2019s really helped pioneer that space,\u201d says Collins, who is at MIT. (The two have not collaborated in the laboratory, but Collins has long been at the forefront of using AI for drug discovery, including the search for antibiotics. In 2020, Collins\u2019s team used an AI model to predict a broad-\u00adspectrum antibiotic, halicin, that is now in preclinical development.)\u00a0<\/p>\n

The world of antibiotic development needs as much creativity and innovation as researchers can muster, says Collins. And de la Fuente\u2019s work on peptides has pushed the field forward: \u201cC\u00e9sar is marvelously talented, very innovative.\u201d\u00a0<\/p>\n

A messy, noisy endeavor<\/h3>\n

De la Fuente describes antimicrobial resistance as an \u201calmost impossible\u201d problem, but he sees plenty of room for exploration in the word almost. <\/em>\u201cI like challenges,\u201d he says, \u201cand I think this is the ultimate challenge.\u201d\u00a0<\/p>\n

The use, overuse, and misuse of antibiotics, he says, drives antimicrobial resistance. And the problem is growing unchecked because conventional ways to find, make, and test the drugs are prohibitively expensive and often lead to dead ends. \u201cA lot of the companies that have attempted to do antibiotic development in the past have ended up folding because there\u2019s no good return on investment at the end of the day,\u201d he says.<\/p>\n

Antibiotic discovery has always been a messy, noisy endeavor, driven by serendipity and fraught with uncertainty and misdirection. For decades, researchers have largely relied on brute-force mechanical methods. \u201cScientists dig into soil, they dig into water,\u201d says de la Fuente. \u201cAnd then from that complex organic matter they try to extract antimicrobial molecules.\u201d\u00a0<\/p>\n

But molecules can be extraordinarily complex. Researchers have estimated the number of possible organic combinations that could be synthesized at somewhere around 1060<\/sup>. For reference, Earth contains an estimated 1018<\/sup> grains of sand. \u201cDrug discovery in any domain is a statistics game,\u201d says Jonathan Stokes, a chemical biologist at McMaster University in Canada, who has been using generative AI to design potential new antibiotics that can be synthesized in a lab, and who worked with Collins on halicin. \u201cYou need enough shots on goal to happen to get one.\u201d\u00a0<\/p>\n

Those have to be good shots, though. And AI seems well suited to improving researchers\u2019 aim. Biology is an information source, de la Fuente explains: \u201cIt\u2019s like a bunch of code.\u201d The code of DNA has four letters; proteins and peptides have 20, where each \u201cletter\u201d represents an amino acid. De la Fuente says his work amounts to training AI models to recognize sequences of letters that encode antimicrobial peptides, or AMPs. \u201cIf you think about it that way,\u201d he says, \u201cyou can devise algorithms to mine the code and identify functional molecules, which can be antimicrobials. Or antimalarials. Or anticancer agents.\u201d\u00a0<\/p>\n

Practically speaking, we\u2019re still not there: These peptides haven\u2019t yet been transformed into usable drugs that help people, and there are plenty of details\u2014dosage, delivery, specific targets\u2014that need to be sorted out, says de la Fuente. But AMPs are appealing because the body already uses them.They\u2019re a critical part of the immune system and often the first line of defense against pathogenic infections. Unlike conventional antibiotics, which typically have one trick for killing bacteria, AMPs often exhibit a multimodal approach. They may disrupt the cell wall and the genetic material inside as well as a variety of cellular processes. A bacterial pathogen may evolve resistance to a conventional drug\u2019s single mode of action, but maybe not to a multipronged AMP attack.<\/p>\n

From discovery to delivery<\/h3>\n

De la Fuente\u2019s group is one of many pushing the boundaries of using AI for antibiotics. Where he focuses primarily on peptides, Collins works on small-molecule discovery. So does Stokes, at McMaster, whose models identify promising new molecules and predict whether they can be synthesized. \u201cIt\u2019s only been a few years since folks have been using AI meaningfully in drug discovery,\u201d says Collins.\u00a0<\/p>\n

Even in that short time the tools have changed, says James Zou, a computer scientist at Stanford University, who has worked with Stokes and Collins. Researchers have moved from using predictive models to developing generative approaches. With a predictive approach, Zou says, researchers screen large libraries of candidates that are known to be promising. Generative approaches offer something else: the appeal of designing a new molecule from scratch. Last year, for example, de la Fuente\u2019s team used one generative AI model to design a suite of synthetic peptides and another to assess them. The group tested two of the resulting compounds on mice infected with a drug-resistant strain of Acinetobacter baumannii<\/em>, a germ that the World Health Organization has identified as a \u201ccritical priority\u201d in research on antimicrobial resistance. Both successfully and safely treated the infection.\u00a0<\/p>\n

But the field is still in the discovery phase. In his current work, de la Fuente is trying to get candidates closer to clinical testing. To that end, his team is developing an ambitious multimodal model called ApexOracle that\u2019s designed to analyze a new pathogen, pinpoint its genetic weaknesses, match it to antimicrobial peptides that might work against it, and then predict how an antibiotic, built from those peptides, would fare in lab tests. It \u201cconverges understanding in chemistry, genomics, and language,\u201d he says. It\u2019s preliminary, he adds, but even if it doesn\u2019t work perfectly, it will help steer the next generation of AI models toward the ultimate goal of resisting resistance.\u00a0<\/p>\n

Using AI, he believes, human researchers now have a fighting chance at catching up to the giant threat before them. The technology has already saved decades of human research time. Now he wants it to save lives, too: \u201cThis is the world that we live in today, and it\u2019s incredible.\u201d\u00a0<\/p>\n

Stephen Ornes is a science writer in Nashville, Tennessee.<\/em><\/p>","protected":false},"excerpt":{"rendered":"

When he was just a teenager trying to decide what to do with his life, C\u00e9sar de la Fuente compiled a list of the world\u2019s biggest problems. He ranked them inversely by how much money governments were spending to solve them. Antimicrobial resistance topped the list.\u00a0 Twenty years on, the problem has not gone away. If anything, it\u2019s gotten worse. Infections caused by bacteria, fungi, and viruses that have evolved ways to evade treatments are now associated with more than 4 million deaths per year, and a recent analysis, published in the Lancet, predicts that number could surge past 8 million by 2050. In a July 2025 essay in Physical Review Letters, de la Fuente, now a bioengineer and computational biologist, and synthetic biologist James Collins warned of a looming \u201cpost\u00adantibiotic\u201d era in which infections from drug-resistant strains of common bacteria like Escherichia coli or Staphylococcus aureus, which can often still be treated by our current arsenal of medications, become fatal. \u201cThe antibiotic discovery pipeline remains perilously thin,\u201d they wrote, \u201cimpeded by high development costs, lengthy timelines, and low returns on investment.\u201d But de la Fuente is using artificial intelligence to bring about a different future. His team at the University of Pennsylvania is training AI tools to search genomes far and deep for peptides with antibiotic properties. His vision is to assemble those peptides\u2014molecules made of up to 50 amino acids linked together\u2014into various configurations, including some never seen in nature. The results, he hopes, could defend the body against microbes that withstand traditional treatments.\u00a0 His quest has unearthed promising candidates in unexpected places. In August 2025 his team, which includes 16 scientists in Penn\u2019s Machine Biology Group, described peptides hiding in the genetic code of ancient single-celled organisms called archaea. Before that, they\u2019d excavated a list of candidates from the venom of snakes, wasps, and spiders. And in an ongoing project de la Fuente calls \u201cmolecular de-\u00adextinction,\u201d he and his collaborators have been scanning published genetic sequences of extinct species for potentially functional molecules. Those species include hominids like Neanderthals and Denisovans and charismatic megafauna like woolly mammoths, as well as ancient zebras and penguins. In the history of life on Earth, de la Fuente reasons, maybe some organism evolved an antimicrobial defense that could be helpful today. Those long-gone codes have given rise to resurrected compounds with names like \u00admammuthusin-2 (from woolly mammoth DNA), mylodonin-2 (from the giant sloth), and hydrodamin-1 (from the ancient sea cow). Over the last few years, this molecular binge has enabled de la Fuente to amass a library of more than a million genetic recipes. At 40 years old, de la Fuente has also collected a trophy case of awards from the American Society for Microbiology, the American Chemical Society, and other organizations. (In 2019, this magazine named him one of \u201c35 Innovators Under 35\u201d for bringing computational approaches to antibiotic discovery.) He\u2019s widely recognized as a leader in the effort to harness AI for real-world problems. \u201cHe\u2019s really helped pioneer that space,\u201d says Collins, who is at MIT. (The two have not collaborated in the laboratory, but Collins has long been at the forefront of using AI for drug discovery, including the search for antibiotics. In 2020, Collins\u2019s team used an AI model to predict a broad-\u00adspectrum antibiotic, halicin, that is now in preclinical development.)\u00a0 The world of antibiotic development needs as much creativity and innovation as researchers can muster, says Collins. And de la Fuente\u2019s work on peptides has pushed the field forward: \u201cC\u00e9sar is marvelously talented, very innovative.\u201d\u00a0 A messy, noisy endeavor De la Fuente describes antimicrobial resistance as an \u201calmost impossible\u201d problem, but he sees plenty of room for exploration in the word almost. \u201cI like challenges,\u201d he says, \u201cand I think this is the ultimate challenge.\u201d\u00a0 The use, overuse, and misuse of antibiotics, he says, drives antimicrobial resistance. And the problem is growing unchecked because conventional ways to find, make, and test the drugs are prohibitively expensive and often lead to dead ends. \u201cA lot of the companies that have attempted to do antibiotic development in the past have ended up folding because there\u2019s no good return on investment at the end of the day,\u201d he says. Antibiotic discovery has always been a messy, noisy endeavor, driven by serendipity and fraught with uncertainty and misdirection. For decades, researchers have largely relied on brute-force mechanical methods. \u201cScientists dig into soil, they dig into water,\u201d says de la Fuente. \u201cAnd then from that complex organic matter they try to extract antimicrobial molecules.\u201d\u00a0 But molecules can be extraordinarily complex. Researchers have estimated the number of possible organic combinations that could be synthesized at somewhere around 1060. For reference, Earth contains an estimated 1018 grains of sand. \u201cDrug discovery in any domain is a statistics game,\u201d says Jonathan Stokes, a chemical biologist at McMaster University in Canada, who has been using generative AI to design potential new antibiotics that can be synthesized in a lab, and who worked with Collins on halicin. \u201cYou need enough shots on goal to happen to get one.\u201d\u00a0 Those have to be good shots, though. And AI seems well suited to improving researchers\u2019 aim. Biology is an information source, de la Fuente explains: \u201cIt\u2019s like a bunch of code.\u201d The code of DNA has four letters; proteins and peptides have 20, where each \u201cletter\u201d represents an amino acid. De la Fuente says his work amounts to training AI models to recognize sequences of letters that encode antimicrobial peptides, or AMPs. \u201cIf you think about it that way,\u201d he says, \u201cyou can devise algorithms to mine the code and identify functional molecules, which can be antimicrobials. Or antimalarials. Or anticancer agents.\u201d\u00a0 Practically speaking, we\u2019re still not there: These peptides haven\u2019t yet been transformed into usable drugs that help people, and there are plenty of details\u2014dosage, delivery, specific targets\u2014that need to be sorted out, says de la Fuente. But AMPs are appealing because the body already uses them.They\u2019re a critical part of the immune system and often<\/p>","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"pmpro_default_level":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"_pvb_checkbox_block_on_post":false,"footnotes":""},"categories":[52,5,7,1],"tags":[],"class_list":["post-71536","post","type-post","status-publish","format-standard","hentry","category-ai-club","category-committee","category-news","category-uncategorized","pmpro-has-access"],"acf":[],"yoast_head":"\nThe scientist using AI to hunt for antibiotics just about everywhere - 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