Machine learning, and representation learning in particular, has the potential to facilitate drug discovery by screening a large chemical space in silico. A successful approach for representing molecules is to treat them as a graph and utilize graph neural networks. One of the key limitations of such methods is the necessity to represent compounds with different numbers of atoms, which requires aggregating the atom's information. Common aggregation operators, such as averaging, result in loss of information at the atom level. In this work, we propose a novel aggregating approach where each atom is weighted non-linearly using the Boltzmann distribution with a hyperparameter analogous to temperature. We show that using this weighted aggregation improves the ability of the gold standard message-passing neural network to predict antibiotic activity. Moreover, by changing the temperature hyperparameter, our approach can reveal the atoms that are important for activity prediction in a smooth and consistent way, thus providing a novel, regulated attention mechanism for graph neural networks. We further validate our method by showing that it recapitulates the functional group in beta-Lactam antibiotics. The ability of our approach to rank the atoms' importance for a desired function can be used within any graph neural network to provide interpretability of the results and predictions at the node level.

利用图神经网络中的一种新的聚合方法，通过非线性加权的玻尔兹曼分布改善传统融合方法的局限性，以提高黄金标准信息传递神经网络对抗生素活性的预测能力，并实现对结果的节点级解释性。

SAF：用于揭示原子重要性排名和提高药物发现中预测率的智能聚合框架