在生成性药物发现中超越SMILES计数以进行数据增强
Helena Brinkmann1, Antoine Argante1, Hugo Ter Steege1
1Institute for Complex Molecular Systems (ICMS), Eindhoven AI Systems Institute (EAISI), Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven The Netherlands f.grisoni@tue.nl.
概括
针对小分子数据集的新型数据增强技术改善了生成性深度学习. 像原子掩盖和令牌删除这样的新方法增强了新分子设计,特别是在低数据场景中.
科学领域:
- 计算化学的计算化学
- 机器学习 机器学习
- 药物发现 药物发现 药物发现
背景情况:
- 小分子数据集限制了生成的深度学习模型.
- 数据增强,特别是SMILES计数,对于新的分子设计至关重要.
- 现有的SMILES增强方法可能无法充分利用化学和语言信息.
研究的目的:
- 研究新的SMILES增强技术,以增强新的分子设计.
- 引入和评估以自然语言处理和化学为灵感的新策略.
- 扩大用于在数据稀缺环境中设计具有特定性质的分子的工具包.
主要方法:
- 开发了四种新的SMILES增强策略:令牌删除,原子掩盖,生物异构体替代和自我训练.
- 将这些方法应用于用于生成深度学习的小分子数据集.
- 系统地分析了每个策略的性能和优势.
主要成果:
- 所有引入的SMILES增强策略都显示了改进新设计的潜力.
- 原子掩盖在低数据模式下学习物理化学性质方面被证明是有效的.
- 符号删除在产生新型分子支架方面表现有前途.
结论:
- 重新思考SMILES增强技术可以显著提高分子设计的生成深度学习.
- 开发的策略为解决小数据集的局限性提供了明显的优势.
- 这种扩展的曲目为化学家提供了更强大的工具来设计具有定制性质的分子.
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