With the goal of minimizing any such conformational dependence, our idea was to design a function inverse to the SMILES generator -that is, one that would estimate encoded 3D information from 2D molecular representation. To do so, we used a teacher-student training model originally devised for model compression . This training scheme involved teaching a "student" model (here, a multiple layer perceptron, MLP, in Figure ) to mimic the behavior of a "teacher" (here, LSTM module from Figure ) based on the latter's input and output data. In this way, the relationship between 3D features and molecular topology learned by Skalic's model could be -to some extenttransferred to a simpler neural network, allowing this network to estimate molecular similarity in terms of 3D features, within milliseconds and without the more computationally-demanding and force-filed dependent generation of conformers and structure alignment (Figure ). Preparation of the training set for the ESR model. First, we collected a random subset of 6,000 small molecules from the ZINC database and divided it (randomly) into the training and test sets in 5:1 proportion. Next, we used each compound as a seed molecule in the Skalic's shape-captioning generator, thus obtaining, in total, 117,824 vectors corresponding to 68,734 unique SMILES strings (on average, 20 "similars" per one seed, with ca. 2% of "similars" discarded because of SMILES errors, as detected by RDKit ). We kept the entries duplicated with respect to SMILES, since in the generator setting of the original model, several slightly different vectors may lead to the same molecule. Since we wished our teacher-student training procedure to average over this redundancy, the dataset was balanced by introducing sample weights inversely proportional to the number of generated vectors per unique SMILES. For instance, if a given molecule appeared 5 times (each time with a different vector) in the set, each of its occurrences was assigned with a weight of 0.2. We note that 11 seed molecules from the test set generated SMILES overlapping with those present in the training set. This is likely a consequence of the similarity between these 11 seed compounds and the training set, and therefore we discarded them together with their descendants. Finally, we represented SMILES with ECFP4 fingerprints kept as vectors of 2048 integers denoting substructure counts (instead of more commonly used binary values).