Protein Interactions and Networks with Compounds based on Sequences using Deep Learning

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Documentation for package ‘DeepPINCS’ version 1.2.1

• DESCRIPTION file.
• User guides, package vignettes and other documentation.

Help Pages

antiviral_drug	List of antiviral drugs with SMILES strings
cnn_in_out	Input and output tensors of encoders
example_bioassay	Example Data for PubChem AID1706 bioassay
example_cci	Example Data for Chemical-Chemical Interactions
example_chem	Example Data for Compounds
example_cpi	Example Data for Compound-Protein Interactions
example_pd	Example Data for Primer-Dimer
example_ppi	Example Data for Protein-Protein Interactions
example_prot	Example Data for Proteins
fit_cpi	Deep learning model fitting and prediction for compound-protein interactions
gcn_in_out	Input and output tensors of encoders
get_canonical_smiles	Convert SMILES strings to canonical SMILES strings
get_fingerprint	Molecular fingerprint of compounds from SMILES strings
get_graph_structure_node_feature	Graph structure and node features from SMILES strings
get_seq_encode_pad	Vectorization of characters of strings
metric_concordance_index	Concordance index
metric_f1_score	F1-score
mlp_in_out	Input and output tensors of encoders
multiple_sampling_generator	Generator function for multiple inputs
predict_cpi	Deep learning model fitting and prediction for compound-protein interactions
rnn_in_out	Input and output tensors of encoders
SARS_CoV2_3CL_Protease	Amino Acid Sequence for the SARS coronavirus 3C-like Protease
seq_check	Check SMILES strings and amino acid sequences
seq_preprocessing	Preprocessing for SMILES strings and amino acid sequences