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Fig. 2 | Journal of Cheminformatics

Fig. 2

From: DeepGraphMolGen, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

Fig. 2

The property prediction pipeline for our method. The steps in green represent the feature extraction using Graph Convolution and the steps in orange represent regression of property scores. a The molecule is represented is a feature vector with features described as in Sect. “Molecular property prediction”. b The feature vector is passed through a linear layer to get Depth-0 message. c Through repeated graph convolution (message passing) followed by Linear Layer, we get Depth N-1 message. d Each atom’s final message is calculated by summing up the messages (also Graph Convolution) of the neighbouring atoms. e The resultant message is passed through a Linear Layer and the mean of all the atoms is taken to get the final embedding. f The property score is regressed from the graph embedding by a Feed Forward Neural Network. g The loss between predicted property and ground truth property is then backpropagated to change the weights

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Journal of Cheminformatics

ISSN: 1758-2946

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