AI Accelerates Antiviral Discovery for Human Enterovirus 71

New AI technique can uncover antiviral compounds using limited data

Artificial intelligence algorithms have now been combined with traditional laboratory methods to uncover promising drug leads against human enterovirus 71 (EV71), the pathogen behind most cases of hand, foot and mouth disease. The study, published today in Cell Reports Physical Science by researchers at the Perelman School of Medicine at the University of Pennsylvania, showed that reliable antiviral predictions can be made even when only a modest amount of experimental data are available. Using an initial panel of 36 small molecules, the investigators trained a machine learning model to spot certain shapes and chemical features that help stop viruses, scoring each compound's likelihood of blocking EV71. The authors put their AI-chosen shortlist to the test: out of eight compounds, five successfully slowed the virus in cell experiments -- about ten times more hits than traditional screening methods usually deliver. "We are collapsing what used to be months of trial-and-error into days," said César de la Fuente, PhD, a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry. "The approach is especially powerful when time, budget or other constraints limit the amount of data you can generate up front." EV71 infections can escalate from mild rash and fever to severe neurological complications, particularly in children under seven and immunocompromised adults. No FDA-approved antivirals currently target the virus. All five confirmed results were tested using computer simulations, which showed that they stuck to certain spots on the virus, findings which could help future researchers stop the virus from changing shape and entering cells. "We see this as a template for rapid antiviral discovery," added postdoctoral researcher Angela Cesaro, PhD, a study co-author. "Whether the next threat is another enterovirus, an emergent respiratory pathogen or a reemerging virus like polio, our AI-driven method shows that, even with limited data, machine learning can accelerate the development of effective solutions and drive a swift response to future outbreaks." The work included collaboration with Procter & Gamble and Cornell University. Research reported in this publication was supported by the Langer Prize (AIChE Foundation), the NIH R35GM138201, DTRA HDTRA1-21-1-0014, and NIAID NIH R01AI149487. Cesar de la Fuente-Nunez is a co-founder of, and scientific advisor, to Peptaris, Inc., provides consulting services to Invaio Sciences, and is a member of the Scientific Advisory Boards of Nowture S.L., Peptidus, European Biotech Venture Builder, the Peptide Drug Hunting Consortium (PDHC), ePhective Therapeutics, Inc., and Phare Bio. The de la Fuente Lab has received research funding or in-kind donations from United Therapeutics, Strata Manufacturing PJSC, and Procter & Gamble; however, only support from Procter & Gamble was used in this work. An invention disclosure associated with this work has been filed.

New AI approach speeds up antiviral discovery for human enterovirus 71

Penn MedicineMay 6 2025 Artificial intelligence algorithms have now been combined with traditional laboratory methods to uncover promising drug leads against human enterovirus 71 (EV71), the pathogen behind most cases of hand, foot and mouth disease. The study, published today in Cell Reports Physical Science by researchers at the Perelman School of Medicine at the University of Pennsylvania, showed that reliable antiviral predictions can be made even when only a modest amount of experimental data are available. AI streamlines the research process Using an initial panel of 36 small molecules, the investigators trained a machine learning model to spot certain shapes and chemical features that help stop viruses, scoring each compound's likelihood of blocking EV71. The authors put their AI-chosen shortlist to the test: out of eight compounds, five successfully slowed the virus in cell experiments-about ten times more hits than traditional screening methods usually deliver. "We are collapsing what used to be months of trial‑and‑error into days," said César de la Fuente, PhD, a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry. "The approach is especially powerful when time, budget or other constraints limit the amount of data you can generate up front." EV71 infections can escalate from mild rash and fever to severe neurological complications, particularly in children under seven and immunocompromised adults. No FDA-approved antivirals currently target the virus. All five confirmed results were tested using computer simulations, which showed that they stuck to certain spots on the virus, findings which could help future researchers stop the virus from changing shape and entering cells. We see this as a template for rapid antiviral discovery. Whether the next threat is another enterovirus, an emergent respiratory pathogen or a reemerging virus like polio, our AI-driven method shows that, even with limited data, machine learning can accelerate the development of effective solutions and drive a swift response to future outbreaks." Angela Cesaro, PhD, postdoctoral researcher, study co-author The work included collaboration with Procter & Gamble and Cornell University. Research reported in this publication was supported by the Langer Prize (AIChE Foundation), the NIH R35GM138201, DTRA HDTRA1-21-1-0014, and NIAID NIH R01AI149487. Figures created with BioRender.com are attributed as such. Molecules were rendered using the PyMOL Molecular Graphics System, Version 3.1.1 Schrödinger, LLC. Cesar de la Fuente-Nunez is a co-founder of, and scientific advisor, to Peptaris, Inc., provides consulting services to Invaio Sciences, and is a member of the Scientific Advisory Boards of Nowture S.L., Peptidus, European Biotech Venture Builder, the Peptide Drug Hunting Consortium (PDHC), ePhective Therapeutics, Inc., and Phare Bio. The de la Fuente Lab has received research funding or in-kind donations from United Therapeutics, Strata Manufacturing PJSC, and Procter & Gamble; however, only support from Procter & Gamble was used in this work. An invention disclosure associated with this work has been filed. Penn Medicine Journal reference: Cesaro, A., et al. (2025). Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning. Cell Reports Physical Science. doi.org/10.1016/j.xcrp.2025.102554.

Machine learning uncovers promising drug leads against human enterovirus 71

University of PennsylvaniaApr 29 2025 Artificial intelligence algorithms have now been combined with traditional laboratory methods to uncover promising drug leads against human enterovirus 71 (EV71), the pathogen behind most cases of hand, foot and mouth disease. The study, published today in Cell Reports Physical Science by researchers at the Perelman School of Medicine at the University of Pennsylvania, showed that reliable antiviral predictions can be made even when only a modest amount of experimental data are available. We see this as a template for rapid antiviral discovery. Whether the next threat is another enterovirus, an emergent respiratory pathogen or a reemerging virus like polio, AI driven methods can help us stay ahead." César de la Fuente, PhD, a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry AI streamlines the research process Using an initial panel of 36 small molecules, the investigators trained a machine learning model to spot certain shapes and chemical features that help stop viruses, scoring each compound's likelihood of blocking EV71. The authors put their AI-chosen shortlist to the test: out of eight compounds, five successfully slowed the virus in cell experiments-about ten times more hits than traditional screening methods usually deliver. "We are collapsing what used to be months of trial‑and‑error into days," said César de la Fuente, PhD, a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry. "The approach is especially powerful when time, budget or other constraints limit the amount of data you can generate up front." EV71 infections can escalate from mild rash and fever to severe neurological complications, particularly in children under seven and immunocompromised adults. No FDA-approved antivirals currently target the virus. All five confirmed results were tested using computer simulations, which showed that they stuck to certain spots on the virus, findings which could help future researchers stop the virus from changing shape and entering cells. "We see this as a template for rapid antiviral discovery," added postdoctoral researcher Angela Cesaro, PhD, a study co-author. "Whether the next threat is another enterovirus, an emergent respiratory pathogen or a reemerging virus like polio, our AI-driven method shows that, even with limited data, machine learning can accelerate the development of effective solutions and drive a swift response to future outbreaks." The work included collaboration with Procter & Gamble and Cornell University. Research reported in this publication was supported by the Langer Prize (AIChE Foundation), the NIH R35GM138201, DTRA HDTRA1-21-1-0014, and NIAID NIH R01AI149487. Figures created with BioRender.com are attributed as such. Molecules were rendered using the PyMOL Molecular Graphics System, Version 3.1.1 Schrödinger, LLC. Cesar de la Fuente-Nunez is a co-founder of, and scientific advisor, to Peptaris, Inc., provides consulting services to Invaio Sciences, and is a member of the Scientific Advisory Boards of Nowture S.L., Peptidus, European Biotech Venture Builder, the Peptide Drug Hunting Consortium (PDHC), ePhective Therapeutics, Inc., and Phare Bio. The de la Fuente Lab has received research funding or in-kind donations from United Therapeutics, Strata Manufacturing PJSC, and Procter & Gamble; however, only support from Procter & Gamble was used in this work. An invention disclosure associated with this work has been filed. University of Pennsylvania Journal reference: Cesaro, A., et al. (2025). Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning. Cell Reports Physical Science. doi.org/10.1016/j.xcrp.2025.102554.

AI technique can uncover antiviral compounds using limited data

by Perelman School of Medicine at the University of Pennsylvania Artificial intelligence algorithms have now been combined with traditional laboratory methods to uncover promising drug leads against human enterovirus 71 (EV71), the pathogen behind most cases of hand, foot and mouth disease. The study, published today in Cell Reports Physical Science by researchers at the Perelman School of Medicine at the University of Pennsylvania, showed that reliable antiviral predictions can be made even when only a modest amount of experimental data are available. Using an initial panel of 36 small molecules, the investigators trained a machine learning model to spot certain shapes and chemical features that help stop viruses, scoring each compound's likelihood of blocking EV71. The authors put their AI-chosen shortlist to the test: out of eight compounds, five successfully slowed the virus in cell experiments -- about ten times more hits than traditional screening methods usually deliver. "We are collapsing what used to be months of trial‑and‑error into days," said de la Fuente. "The approach is especially powerful when time, budget or other constraints limit the amount of data you can generate up front." EV71 infections can escalate from mild rash and fever to severe neurological complications, particularly in children under seven and immunocompromised adults. No FDA-approved antivirals currently target the virus. All five confirmed results were tested using computer simulations, which showed that they stuck to certain spots on the virus, findings which could help future researchers stop the virus from changing shape and entering cells. "We see this as a template for rapid antiviral discovery," added postdoctoral researcher Angela Cesaro, Ph.D., a study co-author. "Whether the next threat is another enterovirus, an emergent respiratory pathogen or a reemerging virus like polio, our AI-driven method shows that even with limited data, machine learning can accelerate the development of effective solutions and drive a swift response to future outbreaks." The work included collaboration with Procter & Gamble and Cornell University.

New AI Technique Can Uncover Antiviral Compounds Using Limited Data | Newswise

Newswise -- PHILADELPHIA - Artificial intelligence algorithms have now been combined with traditional laboratory methods to uncover promising drug leads against human enterovirus 71 (EV71), the pathogen behind most cases of hand, foot and mouth disease. The study, published today in Cell Reports Physical Science by researchers at the Perelman School of Medicine at the University of Pennsylvania, showed that reliable antiviral predictions can be made even when only a modest amount of experimental data are available. "We see this as a template for rapid antiviral discovery," said César de la Fuente, PhD, a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry. "Whether the next threat is another enterovirus, an emergent respiratory pathogen or a reemerging virus like polio, AI driven methods can help us stay ahead." Using an initial panel of 36 small molecules, the investigators trained a machine learning model to spot certain shapes and chemical features that help stop viruses, scoring each compound's likelihood of blocking EV71. The authors put their AI-chosen shortlist to the test: out of eight compounds, five successfully slowed the virus in cell experiments -- about ten times more hits than traditional screening methods usually deliver. "We are collapsing what used to be months of trial‑and‑error into days," said César de la Fuente, PhD, a Presidential Associate Professor of Psychiatry, Microbiology, Bioengineering, Chemical and Biomolecular Engineering, and Chemistry. "The approach is especially powerful when time, budget or other constraints limit the amount of data you can generate up front." EV71 infections can escalate from mild rash and fever to severe neurological complications, particularly in children under seven and immunocompromised adults. No FDA-approved antivirals currently target the virus. All five confirmed results were tested using computer simulations, which showed that they stuck to certain spots on the virus, findings which could help future researchers stop the virus from changing shape and entering cells. "We see this as a template for rapid antiviral discovery," added postdoctoral researcher Angela Cesaro, PhD, a study co-author. "Whether the next threat is another enterovirus, an emergent respiratory pathogen or a reemerging virus like polio, our AI-driven method shows that, even with limited data, machine learning can accelerate the development of effective solutions and drive a swift response to future outbreaks." The work included collaboration with Procter & Gamble and Cornell University. Research reported in this publication was supported by the Langer Prize (AIChE Foundation), the NIH R35GM138201, DTRA HDTRA1-21-1-0014, and NIAID NIH R01AI149487. Figures created with BioRender.com are attributed as such. Molecules were rendered using the PyMOL Molecular Graphics System, Version 3.1.1 Schrödinger, LLC. Cesar de la Fuente-Nunez is a co-founder of, and scientific advisor, to Peptaris, Inc., provides consulting services to Invaio Sciences, and is a member of the Scientific Advisory Boards of Nowture S.L., Peptidus, European Biotech Venture Builder, the Peptide Drug Hunting Consortium (PDHC), ePhective Therapeutics, Inc., and Phare Bio. The de la Fuente Lab has received research funding or in-kind donations from United Therapeutics, Strata Manufacturing PJSC, and Procter & Gamble; however, only support from Procter & Gamble was used in this work. An invention disclosure associated with this work has been filed. ### Penn Medicine is one of the world's leading academic medical centers, dedicated to the related missions of medical education, biomedical research, excellence in patient care, and community service. The organization consists of the University of Pennsylvania Health System and Penn's Raymond and Ruth Perelman School of Medicine, founded in 1765 as the nation's first medical school. The Perelman School of Medicine is consistently among the nation's top recipients of funding from the National Institutes of Health, with $580 million awarded in the 2023 fiscal year. Home to a proud history of "firsts" in medicine, Penn Medicine teams have pioneered discoveries and innovations that have shaped modern medicine, including recent breakthroughs such as CAR T cell therapy for cancer and the mRNA technology used in COVID-19 vaccines. The University of Pennsylvania Health System's patient care facilities stretch from the Susquehanna River in Pennsylvania to the New Jersey shore. These include the Hospital of the University of Pennsylvania, Penn Presbyterian Medical Center, Chester County Hospital, Doylestown Health, Lancaster General Health, Penn Medicine Princeton Health, and Pennsylvania Hospital -- the nation's first hospital, founded in 1751. Additional facilities and enterprises include Good Shepherd Penn Partners, Penn Medicine at Home, Lancaster Behavioral Health Hospital, and Princeton House Behavioral Health, among others.