Fig. 2From: Chemical space exploration based on recurrent neural networks: applications in discovering kinase inhibitorsTraining and molecule generation based on canonical SMILES sequences and randomized SMILES sequences. a The convergence of both models; the loss values were recorded every 200 steps. b, c Similarity of newly generated unique molecules to their closest inhibitors after training with canonical SMILES sequences (b) or randomized SMILES sequences (c) for 1000 steps and 2000 steps. d, e t-SNE plots of combined unique molecules generated through sampling twice after training with canonical SMILES sequences (d) or randomized SMILES sequences (e). 2-D coordinates of CDK4 inhibitors, Pim1 inhibitors and newly generated molecules are colored blue, green and yellow, respectivelyBack to article page