The scheme of the encoder is shown in Figure . The first operation is an increase of the dimensionality of atoms features by a simple linear operation. Then, these vectors are passed to a Graph Convolution Network (GCN) layer implemented in the Pytorch Geometric package . After each GCN layer, the ReLU activation function is applied. The updated atom vectors are passed to the selfattention blocks (Figure ). These blocks are inspired by class attention layers described in the work of Touvron et al. The authors proposed concatenating the training vector with a matrix instead of pooling it. In contrast, in our implementation, the trainable class (or feature) vector is replaced by a vector of the sum of the atom vectors. Following the authors of the article , the multihead attention described in is replaced by the talking-heads attention . Furthermore, the scale layer is used, a trainable diagonal matrix initialised by a constant value (0.005). This matrix is multiplied by the output of the dropout layer before residual summation. After self-attention blocks, the molecular feature vector and the final atom vectors are obtained. The molecular feature vector is used to predict eight pre-calculated structural properties, such as hetero atoms count, H-acceptors and Hdonors count; chiral centres count; rings, hetero rings and aromatic rings and rotatable bonds count. Each property was calculated with ChemAxon 14 cxcalc package. To predict them, eight independent linear layers are employed.