Fig. 3: Performance of the RHML model and important residues identified by the LIME interpreter for traj1. | Nature Communications

Fig. 3: Performance of the RHML model and important residues identified by the LIME interpreter for traj1.

From: Integrative residue-intuitive machine learning and MD Approach to Unveil Allosteric Site and Mechanism for β2AR

Fig. 3

a Three evaluation metrics of clustering for different numbers of clusters (k = 2–7). SSR/SST represents the explained variance wherein values closer to 1 indicate better clustering. Pseudo-F statistic (pSF) measures the cluster separation, and Davies-Bouldin Index (DBI) assesses the cluster similarity. Higher pSF values and lower DBI values indicate better results. b Prediction performance of the interpretable CNN-based multi-classifier for different numbers of clusters (k values). Data are presented as the mean (points) ± standard deviation (error bars) derived from the five-fold cross-validation. c–e Scores of the top 20 important residues identified by the LIME interpreter in deciding the three-classification result such as cluster0 (c), cluster1 (d), and cluster2 (e). f–h Distribution of the top 20 important residues in the 3D structure of β2AR for cluster0 (f), cluster1 (g), and cluster2 (h). The important residues are highlighted in spheres.

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