一个全面的机器学习模型用于金属 - 连接物结合的预测:化学和生物学中的应用.
Erandika Karunaratne1,2, Federico Zahariev1,2, Marilú Pérez García1,2
1Critical Materials Innovation Hub, Ames National Laboratory, Ames, Iowa 50011, United States.
Journal of chemical information and modeling
|October 31, 2025
概括
一个新的机器学习模型使用广泛的实验数据准确地预测金属连接体结合常数. 该工具为各种应用提供了传统方法的快速,经济高效的替代方案.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
背景情况:
- 准确预测金属-合体结合常数对于各种科学和工业应用至关重要.
- 现有的计算方法通常范围有限,专注于特定的金属或联结体家族.
- 需要一种通用,高效和易于使用的工具来预测这些约束常数.
研究的目的:
- 开发和验证一种机器学习模型,用于预测金属-合体结合常量.
- 创建一个超越现有方法的局限性的通用化模型.
- 为密度函数理论 (DFT) 等传统方法提供一个计算效率高的替代方案.
主要方法:
- 利用开源的Chemprop软件开发了一个机器学习模型.
- 在超过30,000个实验LOG K1值上训练模型,包括质子和金属连接体稳定常数.
- 将基于SMILES的分子表示,金属离子描述符和实验条件纳入模型.
主要成果:
- 性能最好的模型获得了0.942的外部测试R2值和0.834.83的MAE.
- 一个简化的"SMILES-only"模型也证明了准确的预测,并保留了约束趋势.
- "仅用于SMILES"模型的性能与使用显著减少计算资源的DFT计算相似.
结论:
- 开发的机器学习模型提供了一个快速,可靠和广泛适用的工具,用于预测金属-连接体结合常量.
- 该模型在生物无机化学,重金属修复和传感器开发等多个领域都表现出有效性.
- 可访问的"SMILES-only"版本是研究人员和行业专业人士缺乏广泛的计算专业知识的宝贵选工具.
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