Input Format

Name	SMILES	Activation Status
Methanethiol	CS	0
Ethanethiol	C(C)S	0
Benzenethiol	C1(=CC=CC=C1)S	1

Parameters

S.No.	Parameter	Type	What it means
1	n_tasks	Integer	Number of tasks
2	graph_conv_layers	List of integer	Width of channels for GCN layers. graph_conv_layers[i] gives the width of channel for the i-th GCN layer
3	activation	String	The activation function to apply to the output of each GCN layer
4	residual	Boolean	Whether to add a residual connection within each GCN layer
5	batchnorm	Boolean	Whether to apply batch normalization to the output of each GCN layer
6	dropout	Float	The dropout probability for the output of each GCN layer
8	predictor_hidden_feats	Integer	The size for hidden representations in the output MLP predictor
9	predictor_dropout	Integer	The dropout probability in the output MLP predictor
10	mode	String	The model type, ‘classification’ or ‘regression’
11	number_atom_features	Integer	The length of the initial atom feature vectors
12	n_classes	Integer	The number of classes to predict per task (only used when mode is ‘classification’)
13	self_loop	Boolean	Whether to add self loops for the nodes, i.e. edges from nodes to themselves. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing.

S.No.	Parameter	Type	What it means
1	n_tasks	Integer	Number of tasks
2	graph_attention_layers	List of integer	Width of channels per attention head for GAT layers. graph_attention_layers[i] gives the width of channel for each attention head for the i-th GAT layer. If both graph_attention_layers and agg_modes are specified, they should have equal length
3	n_attention_heads	Integer	Number of attention heads in each GAT layer
4	residual	Boolean	Whether to add a residual connection within each GAT layer
5	aggregation_modes	List of String	The way to aggregate multi-head attention results for each GAT layer, which can be either ‘flatten’ for concatenating all-head results or ‘mean’ for averaging all-head results. agg_modes[i] gives the way to aggregate multi-head attention results for the i-th GAT layer
6	dropout	Float	The dropout probability for the output of each GAT layer
7	alpha	Float	A hyperparameter in LeakyReLU, which is the slope for negative values
8	predictor_hidden_feats	Integer	The size for hidden representations in the output MLP predictor
9	predictor_dropout	Integer	The dropout probability in the output MLP predictor
10	mode	String	The model type, ‘classification’ or ‘regression’
11	number_atom_features	Integer	The length of the initial atom feature vectors
12	n_classes	Integer	The number of classes to predict per task (only used when mode is ‘classification’)
13	self_loop	Boolean	Whether to add self loops for the nodes, i.e. edges from nodes to themselves. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing.

S.No.	Parameter	Type	What it means
1	n_tasks	Integer	Number of tasks
2	n_atom_feat	Integer	Number of features per atom
3	n_graph_feat	Integer	Number of features for atom in the graph
4	n_outputs	Integer	Number of features for each molecule
5	layer_sizes	List of Integer	List of hidden layer size(s) in the propagation step: length of this list represents the number of hidden layers, and each element is the width of corresponding hidden layer
6	dropout	Float	Dropout probability, applied after each propagation step and gather step
7	layer_sizes_gather	List of Integer	List of hidden layer size(s) in the gather step
8	n_classes	Integer	The number of classes to predict (only used in classification mode)
9	uncertainty	Boolean	If True, include extra outputs and loss terms to enable the uncertainty in outputs to be predicted
10	mode	String	The model type, ‘classification’ or ‘regression’

S.No.	Parameter	Type	What it means
1	n_tasks	Integer	Number of tasks
2	num_layers	Integer	Number of graph neural network layers, i.e. number of rounds of message passing
3	num_timesteps	Integer	Number of time steps for updating graph representations with a GRU
4	graph_feat_size	Int	Size for graph representations
5	number_atom_features	Integer	The length of the initial atom feature vectors
6	dropout	Float	Dropout probability
7	n_classes	Integer	The number of classes to predict per task (only used when mode is ‘classification’)
8	self_loop	Boolean	Whether to add self loops for the nodes, i.e. edges from nodes to themselves. When input graphs have isolated nodes, self loops allow preserving the original feature of them in message passing.

Output Format

SMILES	Activation Status	Probability of Class 0	Probability of Class 1
CC(CCC=C(C)C)CC=O	1	0.1934605	0.8065395
C1CCCCCC(=O)CCCC1	0	0.9994111	0.0005889
CCC(=O)C1=CC=CC=C1	1	0.4913514	0.5086485

How to run pre-trained models

There are two ways users can use saved models to get the predictions on query data:

1. Users can use GetPrediction_From_Checkpoint_deepGraphh.ipynb in the Get Prediction folder on our GitHub.

2. Users can use the Google Collaboratory Notebook

FAQs

What is the prediction based on?

The prediction is based on Graph-based Deep Learning models.

Can I run two prediction models from different tabs of the same browser?

No.

Can I navigate away from the loading screen?

We understand that it can be a little time-consuming, considering the high computations. Please try to be patient, and do not close the tabs, however, you can change the tabs.

What is the use of Datasets Tab?

deepGraphh has provided example datasets for user’s reference.