基于MOR、DOR、KOR、NOR和基于ZOR的交互组网络的阿片类药物使用障碍的机器学习分析
Machine-learning Analysis of Opioid Use Disorder Informed by MOR, DOR, KOR, NOR and ZOR-Based Interactome Networks
摘要：
阿片类药物使用障碍（OUD）在全球范围内持续构成重大的公共卫生挑战和社会影响，阿片类药物依赖性急剧上升，导致潜在的滥用。尽管已经批准了一些药理学试剂用于OUD治疗，但是所述试剂用于OUD的功效需要进一步改进，以提供更安全和更有效的药理学和心理社会治疗。OUD的可行治疗依赖于对阿片依赖神经生物学机制的理解。包括μ、δ、κ、伤害感受素和ζ阿片受体的蛋白质是阿片类药物的直接靶点。每种受体都有一个大的蛋白质相互作用（PPI）网络，当进行各种治疗时表现不同，从而增加了有效阿片类药物成瘾治疗的药物开发过程的复杂性。下面的报告分析了工作，提出了一个PPI网络知情的机器学习研究OUD。我们研究了五种阿片受体网络中的500多种蛋白质，随后收集了74个抑制剂数据集。将梯度提升决策树（GBDT）算法与两种基于高级自然语言处理（NLP）的分子指纹图配对，构建机器学习模型。利用这些模型，我们系统地对四种阿片受体的候选药物的筛选和再利用潜力进行了评估。此外，在筛选潜在候选药物时还考虑了吸收、分布、代谢、排泄和毒性（ADMET）特性。我们的研究可能是一个有价值的和有前途的工具，药物开发的OUD治疗。
英文摘要：
Opioid use disorder (OUD) continuously poses major public health challenges and social implications worldwide with dramatic rise of opioid dependence leading to potential abuse. Despite that a few pharmacological agents have been approved for OUD treatment, the efficacy of said agents for OUD requires further improvement in order to provide safer and more effective pharmacological and psychosocial treatments. Preferable therapeutic treatments of OUD rely on the advances in understanding the neurobiological mechanism of opioid dependence. Proteins including mu, delta, kappa, nociceptin, and zeta opioid receptors are the direct targets of opioids. Each receptor has a large protein-protein interaction (PPI) network, that behaves differently when subjected to various treatments, thus increasing the complexity in the drug development process for an effective opioid addiction treatment. The report below analyzes the work by presenting a PPI-network informed machine-learning study of OUD. We have examined more than 500 proteins in the five opioid receptor networks and subsequently collected 74 inhibitor datasets. Machine learning models were constructed by pairing gradient boosting decision tree (GBDT) algorithm with two advanced natural language processing (NLP)-based molecular fingerprints. With these models, we systematically carried out evaluations of screening and repurposing potential of drug candidates for four opioid receptors. In addition, absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties were also considered in the screening of potential drug candidates. Our study can be a valuable and promising tool of pharmacological development for OUD treatments.
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