Package: GenProSeq
Type: Package
Title: Generating Protein Sequences with Deep Generative Models
Description: Generative modeling for protein engineering is key to
        solving fundamental problems in synthetic biology, medicine,
        and material science. Machine learning has enabled us to
        generate useful protein sequences on a variety of scales.
        Generative models are machine learning methods which seek to
        model the distribution underlying the data, allowing for the
        generation of novel samples with similar properties to those on
        which the model was trained. Generative models of proteins can
        learn biologically meaningful representations helpful for a
        variety of downstream tasks. Furthermore, they can learn to
        generate protein sequences that have not been observed before
        and to assign higher probability to protein sequences that
        satisfy desired criteria. In this package, common deep
        generative models for protein sequences, such as variational
        autoencoder (VAE), generative adversarial networks (GAN), and
        autoregressive models are available. In the VAE and GAN, the
        Word2vec is used for embedding. The transformer encoder is
        applied to protein sequences for the autoregressive model.
Version: 1.17.1
Date: 2024-02-06
Authors@R: c(person(given="Dongmin", family="Jung", email="dmdmjung@gmail.com", role=c("cre", "aut"), comment = c(ORCID = "0000-0001-7499-8422")))
LazyData: FALSE
Depends: keras, mclust, R (>= 4.2)
Imports: tensorflow, word2vec, DeepPINCS, ttgsea, CatEncoders,
        reticulate, stats
Suggests: VAExprs, stringdist, knitr, testthat, rmarkdown
License: Artistic-2.0
biocViews: Software, Proteomics
NeedsCompilation: no
VignetteBuilder: knitr
Config/Bioconductor/UnsupportedPlatforms: windows
Config/pak/sysreqs: cmake libglpk-dev make default-jdk libicu-dev
        libpng-dev libuv1-dev libxml2-dev libssl-dev python3 libx11-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2026-05-29 18:37:14 UTC
RemoteUrl: https://github.com/bioc/GenProSeq
RemoteRef: HEAD
RemoteSha: e67e085a30c6939aa6ac75d3e0ffaf98fb97d7ba
Packaged: 2026-07-06 06:31:08 UTC; root
Author: Dongmin Jung [cre, aut] (ORCID:
    <https://orcid.org/0000-0001-7499-8422>)
Maintainer: Dongmin Jung <dmdmjung@gmail.com>