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

## Documentation for package ‘DeepPINCS’ version 1.4.0

- 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