Effectiveness of the fragment-based molecular property attribution. Above two discoveries were based on attribution results of the proposed method. Meanwhile, the statistical description and mechanism verification also implicitly verified the effectiveness of the fragment-based molecular property attribution. In this section, we further demonstrated the effectiveness and advantage of the proposed attribution method from the following aspects: prediction performance, the spatial distribution of positive and negative molecule activations, and the ability to distinguish the positive causes.
Property prediction task can achieve better performance with fragments than atoms. We trained two graph neural networks (GCNs) [32\cite{bib30}], atom-based and fragment-based, respectively, to achieve prediction property tasks. The essential difference between the two training strategies is that the fragment-based method trains the network by exchanging feature information with the specific fragments in the molecule as the smallest unit. In contrast, the atom-based method only uses atoms. We divided the dataset of each task into training, validation, and test subset following an 8:1:1 ratio, which is used to train the network, select hyperparameters, and test the prediction performance. The other training settings are the same. As shown in Fig. 5A, the fragment-based method performs better than the atom-based GCN on the new dataset of many property tasks. There is a remarkable improvement in the AUROC score from 0.700 to 0.815 for ClinTox dataset with two property tasks. Meanwhile, slight improvement is shown on other tasks of the Tox21 dataset and Sider dataset.
The spatial distribution of positive and negative activations can reveal the reason for the success of the method. We randomly selected 150 molecules with positive and negative labels, respectively (The dataset itself limits the number of selected molecules), and obtained the gradient activations for each molecule with the devised gradient attribution technique, which are used to represent the molecules here. 300 high-dimensional activations were then mapped into two-dimensional space by the t-SNE method [33\cite{bib23}] for better visualization. As shown in Fig. 5B and Fig. S1 (Supporting Information), the gradient activations of positive (blue dots) and negative (red dots) samples are separated, thus showing a cluster effect. Although there is a small amount of coverage at the junction of the two clusters, the boundaries to distinguish the clusters are still clearly visible. Therefore, from the spatial distribution of the gradient activations of positive samples and negative samples, our gradient attribution method can demonstrate the ability to represent critical internal factors for specific property tasks. The full results on the 12 tasks of Tox21 are shown in Fig. S1 (Supporting Information).
The high-dimensional representation outputted from the model can distinguish different positive causes for the property task. We randomly selected 200 positive molecule samples for the Hepatobiliary task. For each molecule, the model outputs a high-dimensional vector, which generally indicates the representation of the molecule in the whole prediction process. These high-dimensional representations were then clustered with the t-SNE method [33\cite{bib23}], and we found that the 200 molecules are mainly divided into several categories. As shown in Fig. 5C, we displayed part of molecules in these clusters ((I)-(VI)), and the mechanism of these molecules was verified. The high-response attribution fragments for each molecule are highlighted with different cluster colors, and we found that the attribution fragments within the same cluster are the same or quite similar. Therefore, the representations of these positive molecules demonstrate a close relationship with the property task, demonstrating our method's effectiveness in exploring the positive cause of each property task.