Fig. 3: The architecture of the RNN generative model and the similarity comparison between the generated molecules and those in the training set. | npj Computational Materials

Fig. 3: The architecture of the RNN generative model and the similarity comparison between the generated molecules and those in the training set.

From: De novo multi-objective generation framework for energetic materials with trading off energy and stability

Fig. 3: The architecture of the RNN generative model and the similarity comparison between the generated molecules and those in the training set.

a The framework consists of a pre-trained RNN and a generated RNN. Each RNN includes three LSTM layers followed by a fully connected layer with a Softmax function. A transfer learning strategy is adopted to generate the energetic molecular space. b The Tanimoto similarity distribution of the Murcko skeleton (left) and the Tanimoto similarity distribution (right) between the generated new molecules and the energetic molecules in the training set.

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