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<p>The Marburg virus (MARV), responsible for severe hemorrhagic fevers with mortality rates as high as 90%, remains a significant public health threat. This study employs machine learning to identify inhibitors targeting the MARV Gene 4 Small ORF protein, crucial for the virus’s replication and immune evasion. The Gene 4 Small ORF protein is pivotal in taking over the host’s cellular mechanisms, facilitating unchecked viral replication and significant immune system disruption. Effective targeting of this protein holds promise for mitigating the viral lifecycle and entry, potentially curbing the severity of the disease outbreaks. A dataset from PubChem, including 301,745 compounds, was utilized to train models like Random Forest (RF), Gradient Boosting Machines (GBM), CatBoost (CB), AdaBoost (AB), and Logistic Regression (LR). The activity outcomes were classified with integers active as 1 and inactive as 0, followed by molecular descriptor generation using RDKit and PaDEL. The models were trained on an 80:20 split and validated on a novel dataset to ensure robustness, with performance metrics such as accuracy and AUC-ROC guiding evaluation. Morgan fingerprints outperformed PubChem fingerprints, achieving higher accuracy (76%), precision (80%), and ROC-AUC (84%). Among the machine learning models evaluated, RF and GBM were the best performers, with RF achieving the highest specificity (83%) and ROC-AUC (0.84). Validation on new datasets further confirmed the effectiveness of these models, with RF and GBM demonstrating strong predictive reliability for identifying potential inhibitors of the Marburg virus. A Web Application known as MARVpred was developed to predict the activity of compounds with anti-MARV properties from the ChEMBL database. MARVpred is freely accessible online (https://igmr.org/software/marvpred). This study signifies a critical step forward in the computational prediction of viral inhibitors, offering a valuable tool for accelerating the development of Marburg virus therapeutics.</p>

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MARVpred: machine learning prediction of inhibitors targeting Marburg virus Gene 4 Small ORF protein

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

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  • Eugene Lamptey,
  • Gabriel Anyaele,
  • Harry Arthur,
  • Thaddeus Adjadeh,
  • Dorothy Sagoe,
  • George Hanson,
  • Endalkachew Girma,
  • Olaitan I. Awe

摘要

The Marburg virus (MARV), responsible for severe hemorrhagic fevers with mortality rates as high as 90%, remains a significant public health threat. This study employs machine learning to identify inhibitors targeting the MARV Gene 4 Small ORF protein, crucial for the virus’s replication and immune evasion. The Gene 4 Small ORF protein is pivotal in taking over the host’s cellular mechanisms, facilitating unchecked viral replication and significant immune system disruption. Effective targeting of this protein holds promise for mitigating the viral lifecycle and entry, potentially curbing the severity of the disease outbreaks. A dataset from PubChem, including 301,745 compounds, was utilized to train models like Random Forest (RF), Gradient Boosting Machines (GBM), CatBoost (CB), AdaBoost (AB), and Logistic Regression (LR). The activity outcomes were classified with integers active as 1 and inactive as 0, followed by molecular descriptor generation using RDKit and PaDEL. The models were trained on an 80:20 split and validated on a novel dataset to ensure robustness, with performance metrics such as accuracy and AUC-ROC guiding evaluation. Morgan fingerprints outperformed PubChem fingerprints, achieving higher accuracy (76%), precision (80%), and ROC-AUC (84%). Among the machine learning models evaluated, RF and GBM were the best performers, with RF achieving the highest specificity (83%) and ROC-AUC (0.84). Validation on new datasets further confirmed the effectiveness of these models, with RF and GBM demonstrating strong predictive reliability for identifying potential inhibitors of the Marburg virus. A Web Application known as MARVpred was developed to predict the activity of compounds with anti-MARV properties from the ChEMBL database. MARVpred is freely accessible online (https://igmr.org/software/marvpred). This study signifies a critical step forward in the computational prediction of viral inhibitors, offering a valuable tool for accelerating the development of Marburg virus therapeutics.