Poly ADP-ribose polymerase 1 (PARP1) is an attractive therapeutic target for cancer treatment. Machine-learning scoring functions constitute a promising approach to discovering novel PARP1 inhibitors. Cutting-edge PARP1-specific machine-learning scoring functions were investigated using semi-synthetic training data from docking activity-labelled molecules: known PARP1 inhibitors, hard-to-discriminate decoys property-matched to them with generative graph neural networks and confirmed inactives. We further made test sets harder by including only molecules dissimilar to those in the training set. Comprehensive analysis of these datasets using five supervised learning algorithms, and protein–ligand fingerprints extracted from docking poses and ligand only features revealed one highly predictive scoring function. This is the PARP1-specific support vector machine-based regressor, when employing PLEC fingerprints, which achieved a high Normalized Enrichment Factor at the top 1% on the hardest test set (NEF1% = 0.588, median of 10 repetitions), and was more predictive than any other investigated scoring function, especially the classical scoring function employed as baseline.

中文翻译：

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全面的机器学习促进基于结构的 PARP1 抑制剂虚拟筛选

聚 ADP-核糖聚合酶 1 (PARP1) 是癌症治疗的一个有吸引力的治疗靶点。机器学习评分函数是发现新型 PARP1 抑制剂的一种有前途的方法。使用来自对接活动标记分子的半合成训练数据研究了尖端的 PARP1 特定机器学习评分函数：已知的 PARP1 抑制剂、难以区分的诱饵（通过生成图神经网络与它们进行属性匹配）以及已确认的非活性物质。我们通过仅包含与训练集中的分子不同的分子，进一步使测试集变得更加困难。使用五种监督学习算法对这些数据集进行综合分析，并从对接姿势和仅配体特征中提取蛋白质-配体指纹，揭示了一种高度预测的评分函数。这是基于 PARP1 特定支持向量机的回归器，当使用 PLEC 指纹时，它在最难的测试集上实现了前 1% 的高标准化富集因子（NEF1% = 0.588，10 次重复的中值），并且更多比任何其他研究的评分函数（尤其是用作基线的经典评分函数）具有预测性。