The rapid progression of machine learning, especially deep learning (DL), has catalyzed a new era in drug discovery, introducing innovative approaches for predicting molecular properties. Despite the many methods available for feature representation, efficiently utilizing rich, high-dimensional information remains a significant challenge. Our work introduces ChemXTree, a novel graph-based model that integrates a Gate Modulation Feature Unit (GMFU) and neural decision tree (NDT) in the output layer to address this challenge. Extensive evaluations on benchmark data sets, including MoleculeNet and eight additional drug databases, have demonstrated ChemXTree’s superior performance, surpassing or matching the current state-of-the-art models. Visualization techniques clearly demonstrate that ChemXTree significantly improves the separation between substrates and nonsubstrates in the latent space. In summary, ChemXTree demonstrates a promising approach for integrating advanced feature extraction with neural decision trees, offering significant improvements in predictive accuracy for drug discovery tasks and opening new avenues for optimizing molecular properties.

中文翻译：

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ChemXTree：用于 ADMET 预测的功能增强的图形神经网络神经决策树框架


机器学习的快速发展，尤其是深度学习 （DL），催化了药物发现的新时代，引入了预测分子特性的创新方法。尽管有许多方法可用于特征表示，但有效利用丰富的高维信息仍然是一项重大挑战。我们的工作介绍了 ChemXTree，这是一种基于图的新型模型，它在输出层集成了门调制特征单元 （GMFU） 和神经决策树 （NDT） 来应对这一挑战。对基准数据集（包括 MoleculeNet 和其他八个药物数据库）的广泛评估表明，ChemXTree 具有卓越的性能，超越或匹配当前最先进的模型。可视化技术清楚地表明，ChemXTree 显著改善了潜在空间中底物和非底物之间的分离。总之，ChemXTree 展示了一种将高级特征提取与神经决策树集成的有前途的方法，显著提高了药物发现任务的预测准确性，并为优化分子特性开辟了新的途径。