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"person_or_org": { "family_name": "Hansen", "given_name": "Lea Gram", "name": "Hansen, Lea Gram", "type": "personal" } }, { "person_or_org": { "family_name": "Tadas", "given_name": "Jako\u010di\u016bnas", "name": "Tadas, Jako\u010di\u016bnas", "type": "personal" } }, { "person_or_org": { "family_name": "Lengger", "given_name": "Bettina", "name": "Lengger, Bettina", "type": "personal" } }, { "person_or_org": { "family_name": "Keasling", "given_name": "Jay", "name": "Keasling, Jay", "type": "personal" } }, { "person_or_org": { "family_name": "Krogh Jensen", "given_name": "Michael", "name": "Krogh Jensen, Michael", "type": "personal" } }, { "person_or_org": { "family_name": "G. Acevedo-Rocha", "given_name": "Carlos", "name": "G. Acevedo-Rocha, Carlos", "type": "personal" } } ], "description": "

The complete set of scripts used to run the chemical space neural networks described in the paper \"Harnessing Chemical Space Neural Networks to Systematically Annotate GPCR ligands\"

\n

01 -- allows a user to search the graph neighbourhood of any smiles compound and create figures and .csv's reporting on available data.

\n

02 -- preprocesses the input data and calculates the sparse adjacancy matrix (high-RAM requirements).

\n

03 -- creates pytorch geometric .pt files for the CSNN.

\n

04 -- train the GNN and RF/MLP models and replicate results (time intensive, hyperparameter search, multiple replicates to assess stability of training).

\n

05 -- train the R128 model, which predicts bioactivity labels for all 128 receptors in a single forward pass!

\n

06 -- inference code for the R6 model (on DTI mode, one compound, its neighbourhood, and the receptor ESM representation). Using the pre-trained models (quick to evaluate.) Allows the user to specify any input .csv and judge predictions.

\n

07 -- inference code for the R128 Model, predicting

\n

08 -- contains other scripts used to retrieve datasets and plot certain figures

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