Shanghai Frontiers Science Center of Artificial Intelligence and Deep Learning and NYU-ECNU Center for Computational Chemistry, NYU Shanghai, Shanghai 200062, China.
Department of Chemistry, New York University, New York, New York 10003, United States.
J Chem Inf Model. 2024 Nov 25;64(22):8440-8452. doi: 10.1021/acs.jcim.4c01186. Epub 2024 Nov 5.
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.
机器学习，特别是深度学习（DL）的快速发展，催生出了药物研发的新时代，为预测分子性质引入了创新方法。尽管有许多方法可用于特征表示，但有效地利用丰富的高维信息仍然是一个重大挑战。我们的工作引入了 ChemXTree，这是一种基于图的新型模型，在输出层中集成了门调制特征单元（GMFU）和神经决策树（NDT），以应对这一挑战。在基准数据集上的广泛评估，包括 MoleculeNet 和另外八个药物数据库，已经证明了 ChemXTree 的卓越性能，超越或匹配了当前最先进的模型。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 为将先进的特征提取与神经决策树相结合提供了一种有前途的方法，为药物发现任务的预测准确性带来了显著提高，并为优化分子性质开辟了新途径。
