Before training, columns (features) with zero variance (computed over the training set) were removed from the data, thus reducing the input (fingerprint) dimensionality to 2044 and output (ESR vectors) from 512 to 196. The model hyperparameters, including L2 regularization and dropout factors, as well as numbers of layers and neurons, were optimized with hyperas package for Bayesian optimization. In order to accelerate this step, we randomly selected 10% of both training and test sets, and then performed 50 optimization trials selecting models with the best score (mean squared error) over the test set. This procedure led to an architecture comprised of two layers with 512 and 196 neurons, respectively. Dropout mask with 0.1 dropout probability was applied to all connections and L2 regularization with coefficient equal to 10 -5 applied to all weights.