SMCseeker: An attentive virtual screening model for antiviral discovery

Influenza virus, with high morbidity and mortality rates, is a global health threat. Traditional antiviral screenings are costly, whereas machine learning could enhance the effectiveness of antiviral drug discovery. Leveraging a large-scale, in-house antiviral dataset against H1N1, we developed a small molecule compound seeker (SMCseeker) framework for identifying highly active anti-H1N1 agents. Data augmentation and a multi-head attention mechanism were utilized to address the extreme data imbalance and enhance the generalization ability of the model. 18,093 structure-activity signatures after cleaning from 52,800 compounds were selected for training, with another 3,876 validation and 3,879 unseen data points to verify the model's generalization ability. H1N1-SMCseeker demonstrates stable performance on validation dataset, unseen dataset, and one experiment, with Positive Predictive Values (PPV) of 70.59%, 70.59%, and 70.65%, respectively. Therefore, H1N1-SMCseeker can effectively identify anti-H1N1 compounds. The SMCseeker framework could potentially be repurposed for discovering antivirals against other medically important viruses.

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Publication Details

Journal
iScience
Published
2025-08-26
DOI
https://doi.org/10.1016/j.isci.2025.113452
Citations
1
Primary Topic
Influenza Virus Research Studies
Type
article
Field-Weighted Citation Impact
1.35
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article

SMCseeker: An attentive virtual screening model for antiviral discovery

Xing‐Yi Ge, Min Guo, Yu Fu, Shuofeng Yuan et al.
1 citations
iScience
Influenza Virus Research Studies
1.35
article

SMCseeker: An attentive virtual screening model for antiviral discovery

Xing‐Yi Ge, Min Guo, Yu Fu, Shuofeng Yuan, Yubin Xie, Ziyao Zhou, Peng Luo, Xiao‐Ou Shu, You‐Qiang Song, Kexin Li, Jing Li, Haoran Sun, Kwok-Yung Yuen, Xiaoyi Guo
article en
1 citations

Abstract

Influenza virus, with high morbidity and mortality rates, is a global health threat. Traditional antiviral screenings are costly, whereas machine learning could enhance the effectiveness of antiviral drug discovery. Leveraging a large-scale, in-house antiviral dataset against H1N1, we developed a small molecule compound seeker (SMCseeker) framework for identifying highly active anti-H1N1 agents. Data augmentation and a multi-head attention mechanism were utilized to address the extreme data imbalance and enhance the generalization ability of the model. 18,093 structure-activity signatures after cleaning from 52,800 compounds were selected for training, with another 3,876 validation and 3,879 unseen data points to verify the model's generalization ability. H1N1-SMCseeker demonstrates stable performance on validation dataset, unseen dataset, and one experiment, with Positive Predictive Values (PPV) of 70.59%, 70.59%, and 70.65%, respectively. Therefore, H1N1-SMCseeker can effectively identify anti-H1N1 compounds. The SMCseeker framework could potentially be repurposed for discovering antivirals against other medically important viruses.

iScienceVol. 28(9)
Hunan University (CN), Shenzhen University (CN), Hong Kong Science and Technology Parks Corporation (HK), Sichuan Agricultural University (CN), Guangzhou Women and Children Medical Center (CN), Shenzhen Technology University (CN), University of Hong Kong (HK), Guangzhou Medical University (CN)
Openalex Percentile: Top 16%
Influenza Virus Research Studies
1.35
DOIOpen Access PDFOpenAlex
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