融合图神经网络与通用描述符，用混合模型提升分子性质预测精度
Improving Predictions of Molecular Properties with Graph Featurisation and Heterogeneous Ensemble Models
- 用GNN提取分子结构特征，结合传统分子描述符
- 在多个数据集上超越顶尖的ChemProp模型表现
- 适合需要高精度分子性质预测的研究者使用
我们提出一种融合学习型分子描述符与通用描述符的混合方法，结合多样化的机器学习模型进行分子性质预测。引入MetaModel框架聚合多个先进模型的预测结果，设计了一种将任务特定的GNN特征与传统分子描述符融合的特征方案。实验表明，该框架在所有回归数据集上均优于当前领先的ChemProp模型，在9个分类数据集中的6个上也表现更优。进一步发现，将ChemProp生成的GNN特征加入集成模型后，可在多个原本表现不佳的数据集上显著提升性能。结论指出：为在广泛任务中实现最佳效果，必须结合通用描述符与任务特化学习特征，并采用多样化模型集成预测。
原文摘要 · Abstract (English)
We explore a "best-of-both" approach to modelling molecular properties by combining learned molecular descriptors from a graph neural network (GNN) with general-purpose descriptors and a mixed ensemble of machine learning (ML) models. We introduce a MetaModel framework to aggregate predictions from a diverse set of leading ML models. We present a featurisation scheme for combining task-specific GNN-derived features with conventional molecular descriptors. We demonstrate that our framework outperforms the cutting-edge ChemProp model on all regression datasets tested and 6 of 9 classification datasets. We further show that including the GNN features derived from ChemProp boosts the ensemble model's performance on several datasets where it otherwise would have underperformed. We conclude that to achieve optimal performance across a wide set of problems, it is vital to combine general-purpose descriptors with task-specific learned features and use a diverse set of ML models to make the predictions.
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