Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.
Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species.
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DOI：
10.1021/acs.chemrestox.2c00283
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发表时间：
2023-02-20
影响因子：
4.1
通讯作者：
Ekins, Sean
中科院分区：
文献类型：
--
作者：
Vignaux, Patricia A.;Lane, Thomas R.;Urbina, Fabio;Gerlach, Jacob;Puhl, Ana C.;Snyder, Scott H.;Ekins, Sean
乙酰胆碱酯酶（AChE）是人类治疗、环境安全和全球食品供应的重要酶和靶标。这种酶的抑制剂也用于消灭害虫，并可能被滥用于自杀或化学战。由于这些有毒化合物的生物放大作用，乙酰胆碱酯酶农药对鱼类、两栖动物和人类等非目标生物体也产生了不利影响。我们详尽地整理了 AChE 抑制数据的公共数据，并为七个不同物种开发了机器学习分类模型。每组模型均针对每个物种和摩根指纹 (ECFP6) 使用多达 9 种不同的算法构建，活性截止值为 1 μM。人类（4075 种化合物）和鳗鱼（5459 种化合物）共识模型使用文献数据中的外部测试集预测 AChE 抑制活性，准确率分别为 81% 和 82%，而相互交叉（准确率 76% 和 82%）不具有物种特异性。此外，我们还创建了人类和鳗鱼 AChE 抑制的机器学习回归模型，以返回所查询分子的预测 IC50 值。我们确实观察到回归模型中物种特异性有所改善，其中基于平均绝对百分比误差（MAPE = 9.73% vs 13.4%），人类 AChE 抑制的人类支持向量回归模型（3652 种化合物）比同一测试集上的鳗鱼回归模型（4930 种化合物）更好地预测了人类测试集的 IC50。这些模型的预测能力肯定受益于训练集化学多样性的增加，通过合并 Tox21 化合物库的数据来扩展我们的人类分类模型就证明了这一点。在我们测试的 10 种化合物中，通过该扩展模型预测其具有活性，其中两种在 100 μM 浓度下显示出 >80% 的抑制作用。因此，这种机器学习方法能够根据 AChE 抑制模型快速对大量分子库进行评分，然后可以选择这些分子库用于未来的体外测试，以识别潜在的毒素。它还使我们能够创建一个公共网站 MegaAChE，用于使用这些模型进行 AChE 抑制的单分子预测，网址为 。
Acetylcholinesterase (AChE) is an important enzyme and target for human therapeutics, environmental safety, and global food supply. Inhibitors of this enzyme are also used for pest elimination and can be misused for suicide or chemical warfare. Adverse effects of AChE pesticides on nontarget organisms, such as fish, amphibians, and humans, have also occurred as a result of biomagnifications of these toxic compounds. We have exhaustively curated the public data for AChE inhibition data and developed machine learning classification models for seven different species. Each set of models were built using up to nine different algorithms for each species and Morgan fingerprints (ECFP6) with an activity cutoff of 1 μM. The human (4075 compounds) and eel (5459 compounds) consensus models predicted AChE inhibition activity using external test sets from literature data with 81% and 82% accuracy, respectively, while the reciprocal cross (76% and 82% percent accuracy) was not species-specific. In addition, we also created machine learning regression models for human and eel AChE inhibition to return a predicted IC50 value for a queried molecule. We did observe an improved species specificity in the regression models, where a human support vector regression model of human AChE inhibition (3652 compounds) predicted the IC50s of the human test set to a better extent than the eel regression model (4930 compounds) on the same test set, based on mean absolute percentage error (MAPE = 9.73% vs 13.4%). The predictive power of these models certainly benefits from increasing the chemical diversity of the training set, as evidenced by expanding our human classification model by incorporating data from the Tox21 library of compounds. Of the 10 compounds we tested that were predicted active by this expanded model, two showed >80% inhibition at 100 μM. This machine learning approach therefore offers the ability to rapidly score massive libraries of molecules against the models for AChE inhibition that can then be selected for future in vitro testing to identify potential toxins. It also enabled us to create a public website, MegaAChE, for single-molecule predictions of AChE inhibition using these models at .
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影响因子：
5.8
作者：
ELLMAN, GL;COURTNEY, KD;FEATHERSTONE, RM
通讯作者：
FEATHERSTONE, RM
影响因子：
8.6
作者：
Aniceto N;Freitas AA;Bender A;Ghafourian T
通讯作者：
Ghafourian T
影响因子：
5.4
作者：
Cheng ZQ;Song JL;Zhu K;Zhang J;Jiang CS;Zhang H
通讯作者：
Zhang H
影响因子：
3.5
作者：
Bajda, Marek;Jonczyk, Jakub;Szymanski, Pawel
通讯作者：
Szymanski, Pawel
影响因子：
4.6
作者：
Guo, Yan;Yang, Hongyu;Liu, Zongliang
通讯作者：
Liu, Zongliang