All attributions were obtained based on the SMILES sequences of molecules and represented each token attribution as ϕ i with i corresponding to each token in the original full string. Analyses of token attributions were analysed in the normalized form were the original ⃗ ϕ attribution vector was divided by the absolute sum of the total string φi = ϕi || ⃗ ϕ|| . We understand ⃗ ϕ to correspond the attributions of the full tokenized string, including original SMILES string, BOS, EOS and PAD tokens. Other analyses include analyses of components of SMILES, atom and alerts (Table ). As mentioned, all analysed components were first normalized with respect to the full tokenized string. We investigated the normalized attributions by comparing distances between attributions, entropy within the attributions and the relative importance given to relative components of the distributions (Table ). Cosine similarity was chosen as a distance measure to analyse the variation of importance over attributions using the implementation from SciPy . Cosine similarity was chosen to measure the agreement of importance given to each of the tokens. Entropy was calculated as a measure of information, similar to Dabkowski and Gal , and relative importance is the fraction of the attribution given to a specific component of the input (Table ), mostly to investigate the overlap between XAI information and human-derived structural alerts .