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Ligand Binding and Linkage

Ligand Binding and Linkage

Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked. In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...

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Updated: May 26, 2025

Defining Substrate Specificities for Lipase and Phospholipase Candidates

Defining Substrate Specificities for Lipase and Phospholipase Candidates

Published on: November 23, 2016

对复杂基质的区域选择性预测设计特定目标数据集

Jules Schleinitz¹, Alba Carretero-Cerdán^1,2, Anjali Gurajapu¹

¹The Warren and Katharine Schlinger Laboratory for Chemistry and Chemical Engineering, Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States.

Journal of the American Chemical Society

|February 21, 2025

概括

此摘要是机器生成的。

机器学习模型可以准确预测C-H功能区域选择性. 积极学习策略有效地处理较小的数据集,优于复杂化学目标的随机选择.

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Last Updated: May 26, 2025

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科学领域:

有机化学
计算化学
机器学习

背景情况:

在C ((sp3) -H功能化中预测区域选择性对于合成化学至关重要.
目前的方法通常依赖于模型基质的直观推断,限制了复杂分子的准确性.

研究的目的:

开发机器学习模型来预测C ((sp3) -H功能化的区域选择性.
研究积极学习策略在为模型培训策划高效数据集中的有效性.
为传统的反应性预测方法提供量化,数据驱动的替代方案.

主要方法:

从现有的文献中编制了二氧化的数据集.
开发和比较各种基于主动学习的数据集选择的获取功能.
在主动学习获取函数中的杆预测反应性和模型不确定性.
在复杂基质和C-H激素化上实验验证工作流程.

主要成果:

具有反应性和不确定性的积极学习获取函数优于基于相似性的方法.
使用获取功能的数据集策划大大减少了所需数据点的数量.
机器设计的,较小的数据集在较大,随机选择的数据集失败时实现了准确的预测.
开发的工作流程表明可用于预测C-H基化中的区域选择性.

结论:

机器学习模型,特别是当在主动学习策划的数据集上进行训练时,为预测C-H功能化区域选择性提供了强大而高效的方法.
这种数据驱动的方法为复杂分子提供了量化和可靠的替代方法.
开发的工作流程简化了预测过程,减少了实验力度,提高了准确性.