标题： 使用 ChemXploreML 进行分子属性预测的机器学习管道 显示英文标题

标题： A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML

摘要： 我们介绍了ChemXploreML，这是一个为基于机器学习的分子性质预测设计的模块化桌面应用程序。该框架的灵活架构允许将任何分子嵌入技术与现代机器学习算法集成，使研究人员能够在不需要广泛编程专业知识的情况下自定义其预测管道。为了展示该框架的能力，我们实现了两种分子嵌入方法——Mol2Vec和VICGAE（方差-不变性-协方差正则化GRU自动编码器）——并与最先进的基于树的集成方法（梯度提升回归、XGBoost、CatBoost和LightGBM）结合使用。使用五个基本分子性质作为测试案例——熔点（MP）、沸点（BP）、蒸气压（VP）、临界温度（CT）和临界压力（CP），我们在CRC化学与物理手册的数据集上验证了我们的框架。模型对于分布良好的属性表现优秀，在临界温度预测中的R$^2$值高达0.93。值得注意的是，虽然Mol2Vec嵌入（300维）提供了稍高的准确率，但VICGAE嵌入（32维）表现出相当的性能，同时提供了显著提高的计算效率。ChemXploreML的模块化设计便于新嵌入技术和机器学习算法的轻松集成，提供了一个灵活的平台用于定制性质预测任务。该应用程序通过直观的界面自动化化学数据预处理（包括基于UMAP的分子空间探索）、模型优化和性能分析，使得复杂的机器学习技术易于访问，同时为高级化学信息学用户提供可扩展性。 显示英文摘要

摘要： We present ChemXploreML, a modular desktop application designed for machine learning-based molecular property prediction. The framework's flexible architecture allows integration of any molecular embedding technique with modern machine learning algorithms, enabling researchers to customize their prediction pipelines without extensive programming expertise. To demonstrate the framework's capabilities, we implement and evaluate two molecular embedding approaches - Mol2Vec and VICGAE (Variance-Invariance-Covariance regularized GRU Auto-Encoder) - combined with state-of-the-art tree-based ensemble methods (Gradient Boosting Regression, XGBoost, CatBoost, and LightGBM). Using five fundamental molecular properties as test cases - melting point (MP), boiling point (BP), vapor pressure (VP), critical temperature (CT), and critical pressure (CP) - we validate our framework on a dataset from the CRC Handbook of Chemistry and Physics. The models achieve excellent performance for well-distributed properties, with R$^2$ values up to 0.93 for critical temperature predictions. Notably, while Mol2Vec embeddings (300 dimensions) delivered slightly higher accuracy, VICGAE embeddings (32 dimensions) exhibited comparable performance yet offered significantly improved computational efficiency. ChemXploreML's modular design facilitates easy integration of new embedding techniques and machine learning algorithms, providing a flexible platform for customized property prediction tasks. The application automates chemical data preprocessing (including UMAP-based exploration of molecular space), model optimization, and performance analysis through an intuitive interface, making sophisticated machine learning techniques accessible while maintaining extensibility for advanced cheminformatics users.