药物发现中的机器学习方法:选择性综述

Ali Abdelkrim, Abdelkrim Bouramoul, Imene Zenbout
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引用次数: 0

摘要

药物开发是制药行业最具挑战性的阶段,因为它非常昂贵和耗时。但是,在以更快的速度和更低的成本生产安全和创新药物的需求日益增加的情况下,重点已经转向通过结合最新的硅技术来增强先导物的识别和先导物在早期发现阶段的优化。在最近的技术中,人工智能(AI)已被引入作为解决所解决问题的强大解决方案,它的结果显着加快了开发过程。其中,机器学习在产生新的候选药物方面发挥了关键作用。在这项工作中,我们介绍了机器学习算法的基本原理,回顾和讨论了它们在药物开发中的应用和当前问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning Methods In Drug Discovery: A Selective Review
Drug development represents the most challenging phase to pharmaceutical industry, as it is extremely expensive and time consumable. But, under increasing demand to produce safe and innovative drugs faster and at a lower cost, the focus has switched to enhance the lead identification and the lead optimization at the early discovery phase by incorporating insilico recent technologies. Among recent technologies, Artificial Intelligence (AI) has been introduced as a powerful solution to the adressed issues, and it results to speed up significantly the development process. Where, machine-learning played a key role in producing fresh drug candidates. In this work, we walk through the fundamentals of machine learning algorithms, review and discuss their application and current issues in drug development.
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