AI-based Mapping of Complex Cannabis Extracts in Pain Pathways

摘要 人们对大麻(作为吸食大麻或以CBD或THC为主的提取物)作为一种 各种健康指征(包括慢性疼痛)的治疗方式。鉴于……的复杂性 然而，我们对大麻在补充和综合保健方面的作用机制知之甚少。 接近了。具体地说，有一种流行的观点认为，100多种大麻素和各种 组成大麻的萜类/黄酮类化合物协同作用，创造了一种“鼓励效应”。一个全面的 需要进行分析，以便更好地了解大麻制剂作为补充药物的潜力。我们 在这里，提出了一种新的人工智能驱动的方法来解决我们知识中的这一差距。 毫不奇怪，在生物体中活跃的天然产物(例如大麻)通常起作用，因为它起作用 如内源性配体或生物体已知的那些。我们假设解构配体 将结构转换成特定的片段将使我们能够识别结合包含这样的内源性靶分子的靶 碎片。此外，我们认为，不同的化合物协同作用将最大限度地进行选择性 小路。我们已经开发了一个人工智能(AI)驱动的平台，DRIVE(药物目标识别 根据化学相似性)，将配基化合物(大麻素和萜类)映射到分子靶标。 因此，我们可以阐明涉及的细胞通路，并预测生理反应。我们将使用漂移来 几种不同大麻提取物(例如，CBD、CBG或THC含量较高)中的概况化合物 止痛特性，以确定治疗组合及其相关的靶点。我们将承担三项任务 明确的目标。在具体目标1中，使用DRIFE对大麻成分进行大规模测绘，以对应 靶蛋白。然后，在具体目标2中，我们将通过评估结合亲和力来扩展漂移平台 利用一种新的神经网络范例(NeuralDock)在代谢物及其蛋白质之间建立联系。我们将使用文本 从PubMed中挖掘复合蛋白质关系的挖掘技术。最后，在具体目标3中，我们将 实验验证了漂移平台的输出，以预测大麻对疼痛的作用机理。我们会 使用传统药理学工具和各种临床前动物模型测试基于人工智能的结果 疼痛。然后，这些相同的模型将被用来通过补充使用 激动剂、拮抗剂、抑制剂和(适当时)基因敲除，以验证机制。 当这些研究成功时，我们将验证漂移是一种新的、有价值的人工智能研究工具 天然产品。此外，我们还将提供有关大麻使用日益增多的重要见解。 相辅相成、综合健康。

ABSTRACT There is tremendous interest in cannabis (as smoked marijuana or CBD- or THC-dominant extracts) as a therapeutic modality for a variety of health indications (including chronic pain). Given the complexity of cannabis, however, we have little insight into its mechanisms of action in complementary and integrative health approaches. Specifically, there is a prevailing notion that the 100+ cannabinoids and the various terpenoids/flavonoids that comprise cannabis act in concert to create an “Entourage Effect”. A comprehensive analysis is required to better understand the potential of cannabis agents as complementary medicines. We herein propose a novel artificial intelligence-driven approach to address this gap in our knowledge. Not surprisingly, a natural product (e.g., cannabis) that is active in an organism typically works because it acts like endogenous ligands or those known to the organism. We hypothesize that deconstructing ligand structures into specific fragments will allow us to identify targets that bind endogenous tergets containing such fragments. Moreover, we believe that disparate compounds acting in concert will maximally engage selective pathways. We have developed an artificial intelligence (AI)-driven platform, DRIFT (drug-target identification based on chemical similarity), to map ligand compounds (cannabinoids and terpenoids) to molecular targets. Thus, we can illuminate involved cellular pathways, and predict physiological response. We will use DRIFT to profile compounds in a number of different cannabis extracts (e.g., high in CBD, CBG, or THC) with varying analgesic properties to identify therapeutic combinations and their relevant targets. We will undertake three specific aims. In Specific Aim 1, use DRIFT for massive mapping of cannabis constituents to corresponding target proteins. Then, in Specific Aim 2, we will extend the DRIFT platform by evaluating binding affinities between metabolites and their proteins using a new neural network paradigm (NeuralDock). We will use text mining techniques to mine compound-protein relationships from PubMed. Finally, in Specific Aim 3, we will experimentally validate the outputs of the DRIFT platform to predict mechanisms of cannabis on pain. We will test the AI-based results using traditional pharmacology tools and a variety of preclinical animal models of pain. These same models will then be employed to test mechanisms through the complementary use of agonists, antagonists, inhibitors and (where appropriate) gene knockouts to validate mechanisms. When these studies are successful, we will have validated DRIFT as a new and valuable AI tool for studying natural products. Moreover, we will provide important insights into the growing use of cannabis in complementary and integrative health.