Quantitative structure-activity relationship (QSAR) models are important tools used in discovering new drug candidates and identifying potentially harmful environmental chemicals. These models often face two fundamental challenges: limited amount of available biological activity data and noise or uncertainty in the activity data themselves. To address these challenges, we introduce and explore a QSAR model based on custom distance metrics in the structure-activity space. The model is built on top of the k-nearest neighbor model, incorporating non-linearity not only in the chemical structure space, but also in the biological activity space. The model is tuned and evaluated using activity data for human estrogen receptor from the US EPA ToxCast and Tox21 databases. The model closely trails the CERAPP consensus model (built on top of 48 individual human estrogen receptor activity models) in agonist activity predictions and consistently outperforms the CERAPP consensus model in antagonist activity predictions. We suggest that incorporating non-linear distance metrics may significantly improve QSAR model performance when the available biological activity data are limited.

中文翻译：

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探索结构活性空间中的非线性距离度量：人类雌激素受体的QSAR模型。

定量构效关系（QSAR）模型是发现新药候选物和鉴定潜在有害环境化学品的重要工具。这些模型通常面临两个基本挑战：有限的可用生物活性数据以及活性数据本身的噪声或不确定性。为了解决这些挑战，我们在结构活动空间中引入并探索了基于自定义距离度量的QSAR模型。该模型建立在k最近邻居模型的基础上，不仅在化学结构空间中而且在生物活性空间中都包含了非线性。使用来自美国EPA ToxCast和Tox21数据库的人类雌激素受体活性数据对模型进行调整和评估。该模型在激动剂活性预测中紧随CERAPP共有模型（建立在48个人类雌激素受体活性模型之上），并在拮抗剂活性预测中始终优于CERAPP共有模型。我们建议当可用的生物活性数据受到限制时，纳入非线性距离指标可能会大大改善QSAR模型的性能。