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使用机器学习自动化药物发现

Ali K Abdul Raheem^1,2, Ban N Dhannoon³

¹College of Information Technology, University of Babylon, Hillah, Babil, Iraq.

Current drug discovery technologies

|June 8, 2023

概括

人工智能 (AI) 和机器学习 (ML) 通过自动化数据分析来加速药物发现. 这些计算方法简化了开发新药的漫长而昂贵的过程.

科学领域:

计算科学计算科学
人工智能的人工智能
机器学习机器学习
药物发现和开发药物发现和开发

背景情况:

传统药物发现是一个复杂,耗时,昂贵的过程,失败率很高.
计算科学的进步,特别是人工智能 (AI),提供了创新的解决方案.
机器学习 (ML) 是人工智能的一个子集,显示出革命药物发现的巨大潜力.

研究的目的:

讨论药物发现和开发管道中涉及的各个阶段.
在这些阶段探索机器学习 (ML) 方法的应用.
概述目前将ML整合到药物发现中的研究.

主要方法:

审查和讨论已建立的药物发现步骤.
识别和解释相关的机器学习 (ML) 技术.
对现有研究的分析,将ML应用于药物发现挑战.

主要成果:

机器学习 (ML) 可以在药物发现中自动化重复的数据处理和分析.
ML技术适用于药物开发管道的多个阶段.
整合ML可以显著减少与药物发现相关的时间,成本和失败率.

结论:

机器学习 (ML) 是提高药物发现效率和成功的强大工具.
采用像机器学习这样的自动化技术对于克服传统方法的局限性至关重要.
进一步研究和应用ML对于未来的制药创新至关重要.

关键词:

机器学习机器学习 "新药设计"的新药设计发现药物的发现.药物属性预测药物属性预测毒品代表机构的代表.药物目标相互作用

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