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相关概念视频

Ligand Binding Sites02:40

Ligand Binding Sites

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Proteins are dynamic macromolecules that carry out a wide variety of essential processes; however, the activities of most proteins depend on their interactions with other molecules or ions, known as ligands.
Protein-ligand interactions are quite specific; even though numerous potential ligands surround a cellular protein at any given time, only a particular ligand can bind to that protein. Moreover, a ligand binds only to a dedicated area on the surface of the protein, known as the...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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相关实验视频

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Quantitative Structure-Activity Relationship, Activity Prediction, and Molecular Dynamics of Non-nucleotide Reverse Transcriptase Inhibitors
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使用ChemXploreML进行分子性质预测的机器学习管道.

Aravindh Nivas Marimuthu1, Brett A McGuire1,2

  • 1Department of Chemistry, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States.

Journal of chemical information and modeling
|May 20, 2025
PubMed
概括

ChemXploreML是一个新的桌面应用程序,用于机器学习分子性质预测. 它集成了各种嵌入方法和ML算法,实现了像临界温度这样的属性的高精度.

科学领域:

  • 化学信息学 化学信息学
  • 计算化学计算化学
  • 机器学习 机器学习

背景情况:

  • 对分子性质的准确预测对于药物发现和材料科学至关重要.
  • 现有的工具往往缺乏灵活性,无法整合各种机器学习模型和分子表示.
  • 需要用户友好的平台来民主化对先进化学信息技术的访问.

研究的目的:

  • 介绍ChemXploreML,一种基于机器学习的模块化桌面应用程序,用于基于机器学习的分子性质预测.
  • 使研究人员能够通过整合各种分子嵌入技术和机器学习算法来定制预测管道.
  • 使用已建立的分子性质数据集来证明框架的实用性和性能.

主要方法:

  • 实现了ChemXploreML,具有灵活的架构,支持各种嵌入方法和ML算法.
  • 评估了Mol2Vec和VICGAE (变异-不变-共变调整的GRU自动编码器) 的嵌入,与梯度增强,XGBoost,CatBoost和LightGBM相结合.
  • 使用CRC手册数据验证了预测点,沸点,蒸汽压力,临界温度 (CT) 和临界压力的框架.

主要成果:

  • 对于分布良好的属性,获得了优异的预测性能,临界温度的R平方值高达0.93.
  • Mol2Vec嵌入式 (300D) 显示了略高的准确性,而VICGAE嵌入式 (32D) 提供了可比性能,提高了计算效率.

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  • 证明了框架在自动化化学数据预处理,模型优化和性能分析方面的能力.

    • 结论:

    • ChemXploreML提供了一个灵活和可访问的平台,用于定制的分子性质预测.
    • 模块化设计可轻松集成新型嵌入技术和ML算法.
    • 该应用程序使从初学者到高级用户的研究人员能够利用化学信息学中复杂的机器学习.