Scholarlink

<p>Structure-based virtual screening (SBVS) is a cornerstone of modern drug discovery pipelines, yet conventional scoring functions often lack the resolution to model complex protein–ligand interactions accurately. To overcome these limitations, we developed DeepCGASPred, a cyclic GMP-AMP synthase (cGAS)-specific deep learning scoring function that integrates three-dimensional (3D) convolutional neural networks (CNNs) with multi-head attention mechanisms and composite structural descriptors, including Structural Protein–Ligand Interaction Fingerprints (SPLIF), hydrogen bond features, and extended connectivity fingerprints (ECFP). This integrative approach enables the model to capture spatial, physicochemical, and topological interaction patterns while prioritizing informative regions for accurate classification of active and inactive compounds. DeepCGASPred was trained using a chemically diverse dataset and rigorously validated through systematic hyperparameter optimization and multiple independent runs. Our results demonstrate that the combined feature set SPLIF+Hbonds+ECFP consistently outperforms all tested feature configurations, achieving a precision-recall area under the curve (PR-AUC) of 0.94–0.97, a median precision of approximately 0.99, a median recall of approximately 0.88, and a median F1 score above 0.92 across ten independent runs. Comparative evaluation reveals that DeepCGASPred surpasses established scoring functions such as SMINA, RF-Score, SCORCH, and CNN-Score on this cGAS-specific dataset, particularly in identifying actives under challenging test conditions. A perfect normalized enrichment factor at 1% (NEF1% = 1.00) confirms strong early enrichment performance. Optimal performance was achieved when the attention mechanism was inserted after the first CNN layer, reinforcing its role in enhancing generalization. DeepCGASPred offers a robust, interpretable framework for target-specific SBVS of cGAS inhibitors; the approach may also inform the development of similar tools for other biological targets.</p> Graphical Abstract <p></p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Development and validation of an attention-based cGAS-specific deep learning scoring function for structure-based virtual screening

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月28日

查看全文
  • Muhammad Junaid,
  • Muhammad Zeeshan,
  • Abbas Khan,
  • Wenjin Li

摘要

Structure-based virtual screening (SBVS) is a cornerstone of modern drug discovery pipelines, yet conventional scoring functions often lack the resolution to model complex protein–ligand interactions accurately. To overcome these limitations, we developed DeepCGASPred, a cyclic GMP-AMP synthase (cGAS)-specific deep learning scoring function that integrates three-dimensional (3D) convolutional neural networks (CNNs) with multi-head attention mechanisms and composite structural descriptors, including Structural Protein–Ligand Interaction Fingerprints (SPLIF), hydrogen bond features, and extended connectivity fingerprints (ECFP). This integrative approach enables the model to capture spatial, physicochemical, and topological interaction patterns while prioritizing informative regions for accurate classification of active and inactive compounds. DeepCGASPred was trained using a chemically diverse dataset and rigorously validated through systematic hyperparameter optimization and multiple independent runs. Our results demonstrate that the combined feature set SPLIF+Hbonds+ECFP consistently outperforms all tested feature configurations, achieving a precision-recall area under the curve (PR-AUC) of 0.94–0.97, a median precision of approximately 0.99, a median recall of approximately 0.88, and a median F1 score above 0.92 across ten independent runs. Comparative evaluation reveals that DeepCGASPred surpasses established scoring functions such as SMINA, RF-Score, SCORCH, and CNN-Score on this cGAS-specific dataset, particularly in identifying actives under challenging test conditions. A perfect normalized enrichment factor at 1% (NEF1% = 1.00) confirms strong early enrichment performance. Optimal performance was achieved when the attention mechanism was inserted after the first CNN layer, reinforcing its role in enhancing generalization. DeepCGASPred offers a robust, interpretable framework for target-specific SBVS of cGAS inhibitors; the approach may also inform the development of similar tools for other biological targets.

Graphical Abstract