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Updated: Jun 19, 2025

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从平凡到令人惊的非附加性:驱动因素和对ML模型的影响
Laura Guasch1, Niels Maeder2, John G Cumming2
1Roche Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann- La Roche AG, Basel, 4070, Switzerland. laura.guasch@roche.com.
Journal of computer-aided molecular design
|July 25, 2024
概括
结构-活动关系中的非添加性 (NA) 是罕见的,但具有信息性. 这项研究揭示了LogP差异显著影响了令人惊的NA,影响了机器学习模型的性能并突出了NA.
科学领域:
- 计算化学的计算化学
- 化学信息学 化学信息学
- 药物发现 药物发现 药物发现
背景情况:
- 结构与活动关系 (SAR) 和结构与财产关系 (SPR) 数据中的非附加性 (NA) 是一个罕见但非常有信息的现象.
- NA可以表示形状灵活性,结构重组或实验错误,影响SAR分析和机器学习 (ML) 模型性能.
研究的目的:
- 系统地分析导致NA的各种现象的频率和原因.
- 开发新的描述符,以表征双重转换周期并识别NA趋势.
- 调查特定因素,如LogP差异对令人惊的NA的影响.
主要方法:
- 对20种目标生物活动和4种与ADME相关的物理化学特性进行了系统分析.
- 开发新的描述符来描述和分类双重转化周期,将其分为"令人惊"和"平凡"类别.
- 检查了令人惊的NA周期之间的共同点,并比较了在目标和ADME数据集之间的NA行为.
主要成果:
- 大多数双重转换周期被归类为平凡的,少数人表现出令人惊的NA.
- 识别出LogP差异是导致令人惊的NA的最重要因素.
- 在目标生物活动数据集和ADME属性数据集之间观察到不同的NA行为.
- 机器学习模型在处理高度非添加性数据方面表现出困难.
结论:
- 了解NA的原因和频率对于准确的SAR分析和强大的ML模型开发至关重要.
- LogP差异是令人惊的NA的关键驱动因素,需要在药物设计中仔细考虑.
- 对NA的进一步研究对于提高化学信息学和药物发现领域预测模型的性能至关重要.
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