QSAR methods are widely applied in the drug discovery process, both in the hit‐to‐lead and lead optimization phase, as well as in the drug-approval process. Most QSAR algorithms are limited to using molecules as input and disregard pharmacophores or pharmacophoric features entirely. However, due to the high level of abstraction, pharmacophore representations provide some advantageous properties for building quantitative SAR models. The abstract depiction of molecular interactions avoids a bias towards overrepresented functional groups in small datasets. Furthermore, a well‐crafted quantitative pharmacophore model can generalise to underrepresented or even missing molecular features in the training set by using pharmacophoric interaction patterns only. This paper presents a novel method to construct quantitative pharmacophore models and demonstrates its applicability and robustness on more than 250 diverse datasets. fivefold cross-validation on these datasets with default settings yielded an average RMSE of 0.62, with an average standard deviation of 0.18. Additional cross-validation studies on datasets with 15–20 training samples showed that robust quantitative pharmacophore models could be obtained. These low requirements for dataset sizes render quantitative pharmacophores a viable go-tomethod for medicinal chemists, especially in the lead-optimisation stage of drug discovery projects.

中文翻译：

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QPHAR：定量药效团活性关系：方法和验证

QSAR 方法广泛应用于药物发现过程中，无论是在先导和先导优化阶段，还是在药物批准过程中。大多数 QSAR 算法仅限于使用分子作为输入，完全忽略药效团或药效团特征。然而，由于高度抽象，药效团表示为构建定量 SAR 模型提供了一些有利的特性。分子相互作用的抽象描述避免了对小数据集中过度表示的官能团的偏见。此外，精心设计的定量药效团模型可以通过仅使用药效团相互作用模式来推广到训练集中代表性不足甚至缺失的分子特征。本文提出了一种构建定量药效团模型的新方法，并证明了其在 250 多个不同数据集上的适用性和稳健性。使用默认设置对这些数据集进行五重交叉验证产生 0.62 的平均 RMSE，平均标准偏差为 0.18。对具有 15-20 个训练样本的数据集进行的其他交叉验证研究表明，可以获得稳健的定量药效团模型。这些对数据集大小的低要求使定量药效团成为药物化学家可行的方法，尤其是在药物发现项目的先导优化阶段。18. 对具有 15-20 个训练样本的数据集进行的其他交叉验证研究表明，可以获得稳健的定量药效团模型。这些对数据集大小的低要求使定量药效团成为药物化学家可行的方法，尤其是在药物发现项目的先导优化阶段。18. 对具有 15-20 个训练样本的数据集进行的其他交叉验证研究表明，可以获得稳健的定量药效团模型。这些对数据集大小的低要求使定量药效团成为药物化学家可行的方法，尤其是在药物发现项目的先导优化阶段。