In this work a modified siamese neural network ('RBFENN') was used that adopts concept 1 and 2, but does not let a similarity layer compute distance. The rationale behind using shared weights is that for a given ligand perturbation, either direction (e.g. growing or vanishing an R-group) entails roughly the same statistical fluctuation in RBFE. Because the intended prediction label in this work is SEM, not similarity, a concatenation layer was used to join legs of the neural network. After the concatenation layer, several fully-connected layers were used with decreasing numbers of neurons leading to the final single neuron. All fully-connected layers used in the network used ReLu activation function, whereas the final single neuron used a linear activation function. See figure for a low-level overview of the RBFENN architecture.