Package: DeepPINCS
Type: Package
Title: Protein Interactions and Networks with Compounds based on
        Sequences using Deep Learning
Description: The identification of novel compound-protein interaction
        (CPI) is important in drug discovery. Revealing unknown
        compound-protein interactions is useful to design a new drug
        for a target protein by screening candidate compounds. The
        accurate CPI prediction assists in effective drug discovery
        process. To identify potential CPI effectively, prediction
        methods based on machine learning and deep learning have been
        developed. Data for sequences are provided as discrete symbolic
        data. In the data, compounds are represented as SMILES
        (simplified molecular-input line-entry system) strings and
        proteins are sequences in which the characters are amino acids.
        The outcome is defined as a variable that indicates how strong
        two molecules interact with each other or whether there is an
        interaction between them. In this package, a deep-learning
        based model that takes only sequence information of both
        compounds and proteins as input and the outcome as output is
        used to predict CPI. The model is implemented by using compound
        and protein encoders with useful features. The CPI model also
        supports other modeling tasks, including protein-protein
        interaction (PPI), chemical-chemical interaction (CCI), or
        single compounds and proteins. Although the model is designed
        for proteins, DNA and RNA can be used if they are represented
        as sequences.
Version: 1.19.0
Date: 2023-07-06
Authors@R: c(person(given="Dongmin", family="Jung", email="dmdmjung@gmail.com", role=c("cre", "aut"), comment = c(ORCID = "0000-0001-7499-8422")))
LazyData: TRUE
LazyDataCompression: xz
Depends: keras, R (>= 4.1)
Imports: tensorflow, CatEncoders, matlab, rcdk, stringdist, tokenizers,
        webchem, purrr, ttgsea, PRROC, reticulate, stats
Suggests: knitr, testthat, rmarkdown
License: Artistic-2.0
biocViews: Software, Network, GraphAndNetwork, NeuralNetwork
NeedsCompilation: no
VignetteBuilder: knitr
Config/pak/sysreqs: libglpk-dev make default-jdk libicu-dev libpng-dev
        libxml2-dev libssl-dev python3 libx11-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2025-10-29 15:08:55 UTC
RemoteUrl: https://github.com/bioc/DeepPINCS
RemoteRef: HEAD
RemoteSha: bdbf3fccdacaa3aaf1d520c83d7aa186b623ed96
Packaged: 2026-03-03 08:06:18 UTC; root
Author: Dongmin Jung [cre, aut] (ORCID:
    <https://orcid.org/0000-0001-7499-8422>)
Maintainer: Dongmin Jung <dmdmjung@gmail.com>
Built: R 4.6.0; ; 2026-03-03 08:08:32 UTC; windows
