评估选择如何扭曲生成药物发现的结果
Rıza Özçelik1,2, Francesca Grisoni3,4
1Institute for Complex Molecular Systems and Dept. Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
Journal of cheminformatics
|November 13, 2025
概括
从生成模型评估新的设计是很有挑战性的. 这项研究分析了10亿个分子设计,揭示了图书馆大小对结果的影响,并提出了可靠的生成药物发现的新指标.
科学领域:
- 药物发现 药物发现 药物发现
- 计算化学的计算化学
- 人工智能的人工智能
背景情况:
- 生成型深度学习在药物发现中提供了变革性的潜力.
- 缺乏评估新设计的标准指南,阻碍了模型基准测试和分子选择.
- 当前的评估指标可能会误导,影响对生成模型性能的评估.
研究的目的:
- 批判性地分析和提出改进,以评估深度学习模型产生的新设计.
- 确定影响评估结果的关键因素,并制定更可靠的评估策略.
- 提高药物发现中的生成建模的稳定性和可重复性.
主要方法:
- 训练化学语言模型分析大约10亿个分子设计.
- 调查生成的分子库大小对评估指标的影响.
- 开发新的计算效率指标,用于大规模生成模型评估.
主要成果:
- 分子库的大小是评估生成模型的重要混因素,往往导致误导性比较.
- 像独特性和分布式相似性这样的常见指标都有陷,扭曲了绩效评估.
- 增加设计数量和使用新的指标可以提高评估可靠性.
结论:
- 对于产生性药物发现的既定评估实践需要改进.
- 较大的分子库和精细的指标对于准确的模型比较和设计选择至关重要.
- 这些发现为药物发现中更可靠和可复制的生成建模提供了指导方针.
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