纯粹:以政策为导向的公正代表,用于结构受约束的分子生成
Abhor Gupta1, Barathi Lenin2,3, Sean Current4
1Robert Bosch Centre for Data Science and AI, Wadhwani School of Data Science and AI (WSAI), Indian Institute of Technology (IIT) Madras, Chennai, 600 036, India. abhorgupta@gmail.com.
Journal of cheminformatics
|October 15, 2025
概括
政策导向的无偏代表 (PURE) 为结构受约束的分子生成提供了一种新的方法. 这种方法克服了药物发现的深度学习的局限性,通过模拟分子转换和学习无偏见的表示.
科学领域:
- 计算化学是一种计算化学.
- 人工智能在药物发现中的作用
- 分子建模分子建模
背景情况:
- 结构受约束分子生成 (SCMG) 的深度学习面临挑战,包括对现有数据的倾向,离散连续空间不匹配和度量泄漏.
- 现有的方法经常与分子固有的离散性质作斗争,并且在培训期间可能会受到特定任务评估指标的偏见.
研究的目的:
- 引入以政策为导向的公正表示 (PURE),这是SCMG的新框架,它解决了当前深度学习的局限性.
- 开发一种方法,通过模拟分子转换来学习高质量,公正的分子表示.
- 改进对药物合成应用的离散分子搜索空间的探索.
主要方法:
- PURE采用了自我监督学习和基于政策的强化学习 (RL) 框架的组合.
- 该方法模拟药物合成环境中的分子转换,避免依赖外部分子指标.
- 基于模板的分子模拟和半监督的训练设计被用来导航离散的分子搜索空间.
主要成果:
- 尽管缺乏指标偏差,但PURE在多个基准指标中实现了与最先进的方法相比具有竞争力或优异的性能.
- 该框架学习高质量的表示,具有固有的任务特定相似性的概念.
- 这项研究表明,PURE在识别类似索拉费尼布的化合物以对抗耐药性的成功应用.
结论:
- 目前对SCMG的深度学习方法需要重新评估,强调需要自然与分子生成问题保持一致的方法.
- PURE为SCMG提供了一个强大而公正的框架,证明了其在药物发现和开发方面的潜力.
- 该方法在识别新型药物候选物方面表现有前途,其应用在打击耐药性的例子.
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