机器学习用于使用化学结构的毒性预测:在现实世界中取得成功的支柱
Srijit Seal1,2, Manas Mahale3, Miguel García-Ortegón2
1Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States.
Chemical research in toxicology
|May 2, 2025
概括
机器学习 (ML) 通过预测分子毒性来帮助药物发现,但需要仔细的数据和验证. 专注于五个支柱提高了ML模型的可靠性,以更快,更好的药物开发决策.
科学领域:
- 计算化学和毒理学计算化学和毒理学
- 药理学和药物开发领域
背景情况:
- 实验性毒性评估是资源密集型的,面临着体内翻译的挑战,限制了数据的可用性.
- 机器学习 (ML) 具有增强或取代药物发现中传统方法的潜力,用于财产和毒性预测.
- 现有的ML应用程序面临来自偏差数据,不适当的算法和不良验证的风险,导致不准确的预测和低于最佳的决策.
研究的目的:
- 突出了解ML模型在药物发现中的预测有效性的关键重要性.
- 强调需要加强对ML模型的理解和应用,以预测毒性.
- 专注于精确定义的数据集,用于小分子毒性预测.
主要方法:
- 该研究强调了基于五个关键支柱的框架,以成功地通过ML驱动分子性质和毒性预测.
- 第1支柱:用于毒性预测的数据集选择.
- 第二支柱:适当的结构表现.
- 第三支柱:合适的模型算法.
- 第四支柱:稳健的模型验证方法.
- 第五支柱:有效地将预测转化为决策.
主要成果:
- 准确的ML预测取决于解决数据偏差,算法选择和验证方法.
- 专注于五个支柱的结构化方法可以减轻在药物发现中与ML相关的风险.
- 改进ML模型的理解和应用对于可靠的毒性预测至关重要.
结论:
- 提高ML模型的理解和应用,特别是使用明确定义的数据集进行毒性预测,对于推动药物发现至关重要.
- 解决五个关键支柱 - 数据选择,结构表示,算法选择,验证和决策翻译 - 将提高ML模型的可靠性.
- 促进ML研究人员和毒理学家之间的合作对于在药物开发中成功实施ML至关重要,这将导致更快的时间表和更好的决策质量.
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