As an alternative to the graph neural network, a simple feedforward dense neural network is tested together with the local many-body tensor representation (L-MBTR). This descriptor is based on histograms of distances and angles between one central atom and its surrounding. It is calculated on the position of the missing hydrogen atom, next to the radical-carrying atom. This position is encoded as a special element 'X', and the reacting hydrogen as 'Y' to present a well-defined task to the network. The L-MBTR descriptor is generated using the DScribe library. . Parts of the descriptor, which correspond to interactions between multiple elements 'X' or 'Y', are always zero and therefore removed to improve efficiency. Three hidden layers of shrinking size (1000, 500, and 100 neurons) are used in the network, utilizing the ReLU activation function, followed by a single output node with linear activation. The hyperparameters were determined by a non-exhaustive manual grid search. Similar to the graph neural network, an ensemble of ten models is trained.