Haneczok Jacek, Delijewski Marcin, Moldzio Rudolf
Erste Digital, Am Belvedere 1, 1100 Vienna, Austria.
Department of Pharmacology, Faculty of Medical Sciences in Zabrze, Medical University of Silesia, Katowice, Poland.
Comput Methods Programs Biomed. 2023 Nov;241:107731. doi: 10.1016/j.cmpb.2023.107731. Epub 2023 Jul 31.
Parkinson's Disease (PD), a common neurodegenerative disorder and one of the major current challenges in neuroscience and pharmacology, may potentially be tackled by the modern AI techniques employed in drug discovery based on molecular property prediction. The aim of our study was to explore the application of a machine learning setup for the identification of the best potential drug candidates among FDA approved drugs, based on their predicted PINK1 expression-enhancing activity.
Our study relies on supervised machine learning paradigm exploiting in vitro data and utilizing the scaffold splits methodology in order to assess model's capability to extract molecular patterns and generalize from them to new, unseen molecular representations. Models' predictions are combined in a meta-ensemble setup for finding new pharmacotherapies based on the predicted expression of PINK1.
The proposed machine learning setup can be used for discovering new drugs for PD based on the predicted increase of expression of PINK1. Our study identified nitazoxanide as well as representatives of imidazolidines, trifluoromethylbenzenes, anilides, nitriles, stilbenes and steroid esters as the best potential drug candidates for PD with PINK1 expression-enhancing activity on or inside the cell's mitochondria.
The applied methodology allows to reveal new potential drug candidates against PD. Next to novel indications, it allows also to confirm the utility of already known antiparkinson drugs, in the new context of PINK1 expression, and indicates the potential for simultaneous utilization of different mechanisms of action.
帕金森病（PD）是一种常见的神经退行性疾病，也是神经科学和药理学当前面临的主要挑战之一，基于分子特性预测的现代人工智能技术可能有助于解决这一问题。我们研究的目的是探索一种机器学习方法的应用，以便在已获美国食品药品监督管理局（FDA）批准的药物中，根据预测的增强PINK1表达活性来识别最具潜力的候选药物。
我们的研究依赖于监督式机器学习范式，利用体外数据并采用支架分割方法，以评估模型提取分子模式并从这些模式推广到新的、未见过的分子表示的能力。模型的预测结果在一个元集成设置中进行组合，以基于PINK1的预测表达找到新的药物疗法。
所提出的机器学习方法可用于基于预测的PINK1表达增加来发现治疗帕金森病的新药。我们的研究确定硝唑尼特以及咪唑烷、三氟甲基苯、酰苯胺、腈、芪和甾体酯的代表物为治疗帕金森病的最具潜力的候选药物，它们在细胞线粒体内外具有增强PINK1表达的活性。
所应用的方法能够揭示治疗帕金森病的新的潜在候选药物。除了新的适应症外，它还能在PINK1表达的新背景下确认已知抗帕金森病药物的效用，并表明同时利用不同作用机制的潜力。
