are learnable weight matrices that apply to the feature vector of the current node and to the aggregated message vector from neighbor nodes, respectively. 𝒎 𝓝(𝒊) (𝒌-𝟏) is the aggregated message from the neighbors 𝓝(𝒊) of node i. The MP layers are followed by multilayer perceptron (MLP) layers to predict pose correctness (node-level task) and the pKd/pKi (graph-level task) as illustrated in Fig. . For node-level predictions, aggregated information from the MP layers is passed to an MLP with Rectified Linear Unit (ReLU) and sigmoid activation function for hidden layers and final layer of MLP, respectively. For graph-level predictions, aggregated information is passed to a readout layer corresponding to a MeanMax pooling and then passed to a two-layers MLP, with ReLu activation function for the hidden layer and linear activation function for the output layer. This allows MLP to leverage energetic information from ensembles of binding poses for ligand-protein affinity predictions.