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使用深度变异异常生成的Fuzz测试分子表示.

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此摘要是机器生成的。

研究人员使用深度学习模型在SELFIES格式中找到无效的分子字符串,揭示了分子表示的弱点,并建议改进以提高可靠性.

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科学领域:

  • 计算化学是一种计算化学.
  • 机器学习是机器学习.
  • 化学信息学 化学信息学

背景情况:

  • 分子表示对于化学信息学和药物发现至关重要.
  • 现有的分子字符串格式需要严格的验证方法.
  • 深度学习模型为测试数据完整性提供了新的途径.

研究的目的:

  • 为了应力测试自我引用嵌入字符串 (SELFIES) 分子格式的稳定性.
  • 识别和分析不正常的SELFIES字符串,这些字符串不会转换为有效的化学结构.
  • 探索无监督深度学习对验证分子表示的实用性.

主要方法:

  • 使用一个变化自动编码器 (VAE),一个无监督的深度学习模型,生成异常的SELFIES字符串.
  • 分析了VAE的潜空间,以确定生产不可转换的SELFIES的区域.
  • 调查了生成的SELFIES字符串无效的结构和化学原因.

主要成果:

  • 成功生成了无法转换为有效的SMILES字符串的异常SELFIES字符串.
  • 确定了与生成无效SELFIES相关的VAE潜伏空间中的特定区域.
  • 发现并非所有SELFIES字符串都可以转换为有效的SMILES,这挑战了以前的假设.

结论:

  • 变量自动编码器是实证压力测试分子表示强度的有效工具.
  • 该研究强调了对SELFIES等分子字符串格式的改进验证协议的需求.
  • 结果提供了关于SELFIES字符串无效性的见解,并建议修改以提高可靠性.