Machine Learning Model for Screening Thyroid Stimulating Hormone Receptor Agonists Based on Updated Datasets and Improved Applicability Domain Metrics

Machine learning (ML) models for screening endocrine-disrupting chemicals (EDCs), such as thyroid stimulating hormone receptor (TSHR) agonists, are essential for sound management of chemicals. Previous models for screening TSHR agonists were built on imbalanced datasets and lacked applicability domain (AD) characterization essential for regulatory application. Herein, an updated TSHR agonist dataset was built, for which the ratio of active to inactive compounds greatly increased to 1:2.6, and chemical spaces of structure–activity landscapes (SALs) were enhanced. Resulting models based on 7 molecular representations and 4 ML algorithms were proven to outperform previous ones. Weighted similarity density (ρs) and weighted inconsistency of activities (IA) were proposed to characterize the SALs, and a state-of-the-art AD characterization methodology ADSAL{ρs, IA} was established. An optimal classifier developed with PubChem fingerprints and the random forest algorithm, coupled with ADSAL{ρs ≥ 0.15, IA ≤ 0.65}, exhibited good performance on the validation set with the area under the receiver operating characteristic curve being 0.984 and balanced accuracy being 0.941 and identified 90 TSHR agonist classes that could not be found previously. The classifier together with the ADSAL{ρs, IA} may serve as efficient tools for screening EDCs, and the AD characterization methodology may be applied to other ML models.用于筛查内分泌干扰物（EDCs）的机器学习（ML）模型（如促甲状腺激素受体（TSHR）激动剂筛选模型），对化学品的科学管理至关重要。既往用于TSHR激动剂筛查的模型均基于不平衡数据集构建，且缺乏监管应用所必需的适用域（AD）表征环节。本研究构建了更新后的TSHR激动剂数据集，其中活性化合物与非活性化合物的比例大幅提升至1:2.6，同时优化了构效关系景观（SALs）的化学空间分布。基于7种分子表征和4种机器学习算法开发的模型，经证实性能优于既往模型。本研究提出加权相似性密度（ρs）与加权活性不一致性（IA）两项指标以表征SALs，并建立了先进的适用域表征方法ADSAL{ρs, IA}。采用PubChem指纹结合随机森林算法，并搭配ADSAL{ρs ≥ 0.15, IA ≤ 0.65}开发的最优分类器，在验证集上展现出优异性能：受试者工作特征曲线（Receiver Operating Characteristic curve，ROC）下面积达0.984，平衡准确率为0.941，且成功识别出此前未被发现的90个TSHR激动剂类别。该分类器与ADSAL{ρs, IA}方法可作为EDCs筛查的高效工具，同时本研究提出的AD表征方法亦可推广至其他机器学习模型。