Figure : Our network architecture consists of two input heads. Each input head consists of one input layer and one hidden layer. The learned feature vectors are concatenated and passed to another fully connected layer. The output of the last layer is a multilabel prediction, 1 if the respective functional group is present and 0 if the functional group is not present.