Machine learning approaches to predict Acetylcholinesterase inhibition

预测乙酰胆碱酯酶抑制的机器学习方法

• 批准号: 10378934
• 负责人: SEAN EKINS
• 金额: $ 25.64万
• 依托单位: COLLABORATIONS PHARMACEUTICALS, INC.
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-12-10 至 2023-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10378934
• 关键词: Acetylcholine; Acetylcholinesterase; Acetylcholinesterase Inhibitors; Address; Adjuvant; Adverse effects; Affect; Algorithms; Amphibia; Applet; Area; Bayesian Modeling; Behavioral; Binding; Biological; Caliber; Cardiovascular system; Cell physiology; Cessation of life; Cognitive; Collaborations; Computer software; Convulsions; Data; Data Set; Databases; Eels; Environment; Environmental Pollution; Enzymes; Equilibrium; Exposure to; Fingerprint; Fishes; Food; Goals; Graph; Human; Knowledge; Lacrimation; Libraries; Life; Literature; Lung; Machine Learning; Mission; Modeling; Mucous body substance; Muscarinic Acetylcholine Receptor; Muscle fasciculation; Neuraxis; Neuromuscular Junction; Neurons; Neurotransmitters; Nicotinic Receptors; Online Systems; Organ; Organism; Organophosphorus Compounds; Peripheral; Pesticides; Pharmaceutical Preparations; Pharmacologic Substance; Phase; Poison; Poisoning; Production; PubMed; Receiver Operating Characteristics; Research; Respiratory distress; Scientist; Seizures; Signaling Molecule; Skin; Source; Status Epilepticus; Structure; Synapses; Testing; Time; Toxic effect; Toxicology; Training; Tremor; Validation; Work; base; chemical property; cholinergic; cholinergic synapse; computing resources; data curation; data integration; data modeling; drug discovery; experience; follow-up; high throughput screening; inhibitor; machine learning model; machine learning pipeline; model building; model development; multiple datasets; nerve agent; prototype; public database; saliva secretion; scaffold; screening; small molecule; software development; tool; wound healing

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.

概括 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 它还在与细胞功能相关的非神经元环境中充当信号分子， 例如增殖和分化，以及执行器官功能，例如皮肤或伤口的愈合 肺部粘液产生是最常见的中毒原因之一。 全世界每年有近 300 万人中毒，导致 30 万人死亡。 大约 8000 种在美国，由于其独特的化学特性，OP 可以与 乙酰胆碱酯酶 (AChE)，使该酶无法水解胆碱能突触中的 ACh 乙酰胆碱的后续积累会导致受影响神经元的过度刺激。 通过毒蕈碱和烟碱受体发挥作用。 过量的全身性乙酰胆碱的外周效应包括 可观察到的毒性体征（例如瞳孔缩小、流泪、流涎、肌束颤动、震颤和抽搐），以及 危及生命的心血管和呼吸窘迫同时进展。 中枢神经系统最终会导致一种持续癫痫发作的状态，称为癫痫持续状态。 OP 诱发的 SE 与广泛的神经损伤以及伴随的认知和认知障碍有关。 除了直接影响人类外，有机磷农药还可以通过暴露于环境中间接影响人类。 环境中本身受到污染的各种类型的生物体。 农药对鱼类、两栖动物和人类等非目标生物体的影响也发生在 有毒化合物生物放大的结果。公共“结构活性/毒性”中缺少什么。 “关系”数据库是可访问的机器学习模型，供科学家用来从中提取知识 我们建议根据正在积累的小分子数据来预测 AChE 的抑制作用。 因此，我们的使命是让各种公共数据集更容易被访问。 通过提供准备建模的底层数据集以及应用预构建模型来进行机器学习建模 因此，这个项目涵盖了自动化管理、数据集成，并将建立一个研究渠道。 用于 AChE 抑制的机器学习模型开发 我们现在建议自动管理公共 AChE。 主要使用小分子/生物活性数据（例如 IC50、Ki、EC50 或抑制百分比）的数据库 等），按目标和物种排序。我们将开发软件来自动管理数据，构建机器学习模型。 并识别抑制人类和其他物种的 AChE 的潜在分子，以预测中毒情况 我们还将使用外部文献数据来验证这些模型。 训练集并了解这些模型对其他类别分子的适用范围 我们的最终目标是提供软件和模型来预测 AChE 抑制，这将是一个 商业产品。