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DOE OSTI · 2570404

Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning

Cesaro, Angela [Univ. of Pennsylvania, Philadelphia, PA (United States)]·Wan, Fangping [Univ. of Pennsylvania, Philadelphia, PA (United States)]·Shi, Haoyuan [Cornell Univ., Ithaca, NY (United States)]·Wang, Kaiyang [Cornell Univ., Ithaca, NY (United States)]·Maupin, C. Mark [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)]·Barker, Matt L. [Procter & Gamble Co., Cincinnati, OH (United States)]·Liu, Jiqian [Procter & Gamble Co. (Singapore)]·Fox, Stephen J. [Procter & Gamble Co., Cincinnati, OH (United States)]·Yeo, Jingjie [Cornell Univ., Ithaca, NY (United States)]·de la Fuente-Nunez, Cesar [Univ. of Pennsylvania, Philadelphia, PA (United States)]
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Abstract

Computational methods have demonstrated success in identifying virucidal agents, effectively contributing to the discovery of novel virucidal molecules. In this study, we developed a machine learning (ML) model, trained on a small dataset, to predict inhibitors of human enterovirus 71 (EV71), a pathological agent that causes severe disease in children and immunocompromised adults. Despite the dataset’s limitation, comprising of only 36 compounds tested, our ML framework demonstrated significant predictive capability. Notably, experimental validation revealed that five out of the eight compounds predicted by our model from the Chinese cosmetic material list exhibited virucidal activity. The inhibitor effects displayed by the main active compounds were further confirmed by molecular dynamics simulation. This underscores the potential of our AI-driven approach to bypass data constraints in identifying active molecules against viral pathogens.

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60 APPLIED LIFE SCIENCESMolecular dynamicsanti-viralmachine learning (ML)

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BibTeXRIS

Cesaro, Angela [Univ. of Pennsylvania, Philadelphia, PA (United States)], Wan, Fangping [Univ. of Pennsylvania, Philadelphia, PA (United States)], Shi, Haoyuan [Cornell Univ., Ithaca, NY (United States)], Wang, Kaiyang [Cornell Univ., Ithaca, NY (United States)], Maupin, C. Mark [Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)], Barker, Matt L. [Procter & Gamble Co., Cincinnati, OH (United States)], Liu, Jiqian [Procter & Gamble Co. (Singapore)], Fox, Stephen J. [Procter & Gamble Co., Cincinnati, OH (United States)], Yeo, Jingjie [Cornell Univ., Ithaca, NY (United States)], de la Fuente-Nunez, Cesar [Univ. of Pennsylvania, Philadelphia, PA (United States)]. 2025-04-29. Antiviral discovery using sparse datasets by integrating experiments, molecular simulations, and machine learning. https://doi.org/10.1016/j.xcrp.2025.102554

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