Machine learning approaches to predict Acetylcholinesterase inhibition

总结 乙酰胆碱（Ach）是自主神经和中枢神经肌肉接头和突触的神经递质 神经系统它还在与细胞功能相关的非神经元环境中作为信号分子发挥作用， 如增殖和分化，以及执行器官功能，如皮肤中的伤口愈合， 肺中的粘液产生。有机磷是最常见的中毒原因之一 国际吧每年有近300万起中毒事件导致30万人死亡 大约有8000人在美国。由于其独特的化学性质，OP结合到 乙酰胆碱酯酶（AChE），使酶不能水解乙酰胆碱在胆碱能突触 和神经肌肉接头。随后ACh的积累导致受影响神经元的过度刺激 通过毒蕈碱和烟碱受体起作用。过量的全身性ACh的外周效应包括 可观察到的毒性迹象（例如，瞳孔缩小、流泪、流涎、肌颤、震颤和惊厥），以及 危及生命的心血管和呼吸窘迫。同时进展的胆碱能危象内 中枢神经系统最终诱发称为癫痫持续状态的持续癫痫发作状态。 未减轻的OP诱导的SE与广泛的神经元损伤以及伴随的认知和 行为缺陷除了对人体的直接影响外，OP还可以通过暴露于 各种类型的生物体本身已经在环境中被污染。的一些不良 农药对非目标生物如鱼类、两栖动物和人类的影响也发生在 有毒化合物的生物放大作用。公共“结构活性/毒性”中缺少什么 关系”数据库是科学家可以使用的机器学习模型，用于从 小分子数据正在积累。我们建议从结构上预测乙酰胆碱酯酶的抑制， 单分子。因此，我们的使命是使各种公共数据集更容易访问， 通过提供可用于建模的底层数据集以及应用预构建模型，进行机器学习建模 我们自己的因此，该项目涵盖自动化策展，数据集成，并将建立一个研究管道 用于AChE抑制的机器学习模型开发。我们现在建议公共乙酰胆碱酯酶的自动化 主要使用小分子/生物活性数据（如IC 50、Ki、EC 50或%抑制）的数据库 等），按目标和物种分类。我们将开发软件来自动管理数据，建立机器学习模型， 并从人类和其他物种中识别出抑制乙酰胆碱酯酶的潜在分子，以预测中毒 以及可能的环境污染。我们还将用文献以外的数据来验证这些模型。 训练集，并了解这些模型的适用范围，以其他类别的分子，除了 行动我们的最终目标是提供预测乙酰胆碱酯酶抑制的软件和模型，这将是一个新的研究领域。 商业产品。

Summary Acetylcholine (Ach) is a neurotransmitter at neuromuscular junctions and synapses in the autonomic and central nervous systems. It also functions as a signaling molecule in non-neuronal contexts related to cellular functions, such as proliferation and differentiation, as well as performing organ functions, like wound healing in skin or mucus production in lungs. Organophosphorus (OP) are one of the most common causes of poisoning worldwide. There are nearly 3 million poisonings per year resulting in three hundred thousand deaths of these approximately 8000 are in the USA. Because of their unique chemical properties, OPs bind to acetylcholinesterase (AChE), rendering the enzyme incapable of hydrolyzing ACh in the cholinergic synapses and neuromuscular junctions. Subsequent accumulation of ACh leads to overstimulation of the affected neurons acting through muscarinic and nicotinic receptors. The peripheral effects of excess systemic ACh include observable toxic signs (e.g., miosis, lacrimation, salivation, fasciculation, tremors and convulsions), as well as life- threatening cardiovascular and respiratory distress. Simultaneous progression of the cholinergic crisis within the central nervous system ultimately induces a state of unremitting seizure known as status epilepticus. Unmitigated OP-induced SE is associated with wide- spread neuronal damage, and concomitant cognitive and behavioral deficits. Besides the effects directly in humans, OPs can reach humans indirectly via expose to various types of organisms that have themselves been contaminated in the environment. Some of the adverse effects of pesticides on non-target organisms such as fish, amphibians and humans have also occurred as a result of biomagnifications of the toxic compounds. What is missing across public “Structure Activity/toxicity Relationship” databases are accessible machine learning models for scientists to use to extract knowledge from the small molecule data that is accumulating. We would propose predicting AChE inhibition from structure of the molecule alone. Our mission is therefore to make the various public datasets much more readily accessible to machine learning modeling by providing the underlying datasets ready to model as well as apply prebuilt models of our own. This project therefore covers automated curation, data integration and will build a research pipeline for machine learning model development for AChE inhibition. We now propose auto-curation of public AChE databases which use predominantly small molecule / biological activity data (such as IC50, Ki, EC50, or % inhibition etc), sorted by target and species. We will develop software to autocurate data, build machine learning models and identify potential molecules that inhibit AChE from human and other species in order to predict poisoning and possible environmental contamination. We will also validate these models with literature data outside of the training sets and understand the applicability domain of these models to other classes of molecules besides OPs. Our ultimate goal will be to provide software and models to predict AChE inhibition which will be a commercial product.