Feng Hongsong, Elladki Rana, Jiang Jian, Wei Guo-Wei
Department of Mathematics, Michigan State University, MI 48824, USA.
Research Center of Nonlinear Science, School of Mathematical and Physical Sciences, Wuhan Textile University, Wuhan, 430200, PR China.
Comput Biol Med. 2023 May;157:106745. doi: 10.1016/j.compbiomed.2023.106745. Epub 2023 Mar 8.
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. Proteins including mu, delta, kappa, nociceptin, and zeta opioid receptors are the direct targets of opioids and play critical roles in therapeutic treatments. The protein-protein interaction (PPI) networks of the these receptors increase the complexity in the drug development process for an effective opioid addiction treatment. The report below presents 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 autoencoder and Transformer fingerprints for molecules. With these models, we systematically carried out evaluations of screening and repurposing potential of more than 120,000 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 machine-learning tools determined a few inhibitor compounds with desired potency and ADMET properties for nociceptin opioid receptors. Our approach offers a valuable and promising tool for the pharmacological development of OUD treatments.
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，阿片类药物依赖急剧增加导致潜在的滥用情况。尽管已有几种药物被批准用于治疗OUD，但这些药物对OUD的疗效仍需进一步提高，以便提供更安全、更有效的药物和心理社会治疗方法。包括μ、δ、κ、孤啡肽和ζ阿片受体在内的蛋白质是阿片类药物的直接靶点，在治疗中起着关键作用。这些受体的蛋白质-蛋白质相互作用（PPI）网络增加了有效治疗阿片类药物成瘾的药物开发过程的复杂性。以下报告介绍了一项基于PPI网络的OUD机器学习研究。我们研究了五个阿片受体网络中的500多种蛋白质，并随后收集了74个抑制剂数据集。通过将梯度提升决策树（GBDT）算法与两种基于先进自然语言处理（NLP）的自动编码器和分子的Transformer指纹配对，构建了机器学习模型。利用这些模型，我们系统地评估了超过120000种药物候选物对四种阿片受体的筛选和重新利用潜力。此外，在筛选潜在药物候选物时还考虑了吸收、分布、代谢、排泄和毒性（ADMET）特性。我们的机器学习工具确定了几种对孤啡肽阿片受体具有所需效力和ADMET特性的抑制剂化合物。我们的方法为OUD治疗的药理学开发提供了一个有价值且有前景的工具。
