diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 272eb15..ffc2927 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -23,7 +23,7 @@ jobs:
         run: |
           python -m pip install --upgrade pip
           pip install flake8 pytest
-          pip install --editable .
+          pip install --editable .[test]
       - name: Lint with flake8
         run: |
           # stop the build if there are Python syntax errors or undefined names
diff --git a/MANIFEST.in b/MANIFEST.in
index 74bda3e..50040f2 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,5 +1,3 @@
-include deeplc/expasy/*
-include deeplc/mod_to_smiles/*
 include deeplc/mods/*
 include deeplc/package_data/**/*
 include deeplc/unimod/*
diff --git a/deeplc/__main__.py b/deeplc/__main__.py
index 4c0e3e8..eb6079f 100644
--- a/deeplc/__main__.py
+++ b/deeplc/__main__.py
@@ -12,17 +12,14 @@
 import warnings
 
 import pandas as pd
-from matplotlib import pyplot as plt
-
-from deeplc import __version__, DeepLC, FeatExtractor
-from deeplc._argument_parser import parse_arguments
-from deeplc._exceptions import DeepLCError
-
 from psm_utils.io.peptide_record import peprec_to_proforma
 from psm_utils.psm import PSM
 from psm_utils.psm_list import PSMList
 from psm_utils.io import read_file
-from psm_utils.io import write_file
+
+from deeplc import __version__, DeepLC, FeatExtractor
+from deeplc._argument_parser import parse_arguments
+from deeplc._exceptions import DeepLCError
 
 logger = logging.getLogger(__name__)
 
diff --git a/deeplc/deeplc.py b/deeplc/deeplc.py
index fd2d3d3..1142162 100644
--- a/deeplc/deeplc.py
+++ b/deeplc/deeplc.py
@@ -31,22 +31,18 @@
 
 LIBRARY = {}
 
-import os
-import sys
 import copy
 import gc
 import logging
+import math
 import multiprocessing
 import multiprocessing.dummy
-import pickle
+import random
+import sys
 import warnings
 from configparser import ConfigParser
-from tempfile import TemporaryDirectory
-from copy import deepcopy
-import random
-import math
-from collections import ChainMap
 from itertools import chain
+from tempfile import TemporaryDirectory
 
 # If CLI/GUI/frozen: disable Tensorflow info and warnings before importing
 IS_CLI_GUI = os.path.basename(sys.argv[0]) in ["deeplc", "deeplc-gui"]
@@ -65,29 +61,25 @@
 import numpy as np
 import pandas as pd
 import tensorflow as tf
-from tensorflow.python.eager import context
-from tensorflow.keras.models import load_model
-import h5py
-
-from deeplc._exceptions import CalibrationError, DeepLCError
-from deeplc.trainl3 import train_en
-
+from deeplcretrainer import deeplcretrainer
+from psm_utils.io import read_file
 from psm_utils.io.peptide_record import peprec_to_proforma
 from psm_utils.psm import PSM
 from psm_utils.psm_list import PSMList
-from psm_utils.io import read_file
-from psm_utils.io import write_file
+from tensorflow.keras.models import load_model
+from tensorflow.python.eager import context
 
-from deeplcretrainer import deeplcretrainer
+from deeplc._exceptions import CalibrationError
+from deeplc.trainl3 import train_en
 
 # "Custom" activation function
 lrelu = lambda x: tf.keras.activations.relu(x, alpha=0.1, max_value=20.0)
 
 
 try:
-    from tensorflow.compat.v1.keras.backend import set_session
+    from tensorflow.compat.v1.keras.backend import set_session  # noqa: F401
 except ImportError:
-    from tensorflow.keras.backend import set_session
+    from tensorflow.keras.backend import set_session  # noqa: F401
 try:
     from tensorflow.compat.v1.keras.backend import clear_session
 except ImportError:
@@ -112,9 +104,10 @@
 # session = tf.compat.v1.Session(config=config)
 
 # Feature extraction
-from deeplc.feat_extractor import FeatExtractor
 from pygam import LinearGAM, s
 
+from deeplc.feat_extractor import FeatExtractor
+
 
 def warn(*args, **kwargs):
     pass
diff --git a/deeplc/feat_extractor.py b/deeplc/feat_extractor.py
index c1cb666..2e3f8ba 100644
--- a/deeplc/feat_extractor.py
+++ b/deeplc/feat_extractor.py
@@ -12,7 +12,6 @@
 __email__ = ["Robbin.Bouwmeester@ugent.be", "Ralf.Gabriels@ugent.be"]
 
 # Native imports
-from operator import index
 import os
 import math
 import time
@@ -20,27 +19,14 @@
 import ast
 from re import sub
 import logging
-from copy import deepcopy
 
-# Numpy
 import numpy as np
-
-# Pandas
 import pandas as pd
-
 from psm_utils.io.peptide_record import peprec_to_proforma
 from psm_utils.psm import PSM
 from psm_utils.psm_list import PSMList
 from pyteomics import mass
 
-from functools import lru_cache
-
-from psm_utils.io.peptide_record import peprec_to_proforma
-from psm_utils.psm import PSM
-from psm_utils.psm_list import PSMList
-from psm_utils.io import read_file
-from psm_utils.io import write_file
-
 logger = logging.getLogger(__name__)
 
 
diff --git a/deeplc/trainl3.py b/deeplc/trainl3.py
index 2720fee..a28e083 100644
--- a/deeplc/trainl3.py
+++ b/deeplc/trainl3.py
@@ -12,35 +12,26 @@
 WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 See the License for the specific language governing permissions and
 limitations under the License.
-This code is used to train retention time predictors and store
-predictions from a CV procedure for further analysis.
 
 This project was made possible by MASSTRPLAN. MASSTRPLAN received funding 
 from the Marie Sklodowska-Curie EU Framework for Research and Innovation 
 Horizon 2020, under Grant Agreement No. 675132.
 """
 
-from sklearn.model_selection import RandomizedSearchCV
-from sklearn.linear_model import ElasticNet
-from sklearn.metrics import mean_absolute_error
-from sklearn.feature_selection import SelectFromModel
-from sklearn.model_selection import cross_val_predict
-from sklearn.model_selection import KFold
-from sklearn.base import clone
-from sklearn.model_selection import GridSearchCV
-from scipy.stats import randint
-from scipy.stats import uniform
-from numpy import arange
-from scipy.stats import pearsonr
+try:
+    from sklearn.base import clone
+    from sklearn.linear_model import ElasticNet
+    from sklearn.model_selection import GridSearchCV
+except ImportError:
+    _has_sklearn = False
+else:
+    _has_sklearn = True
 
-from operator import itemgetter
-from numpy import median
-from collections import Counter
-    
-def train_en(X,y,n_jobs=16,cv=None):
+
+def train_en(X, y, n_jobs=16, cv=None):
     """
     Function that trains Layer 3 of CALLC (elastic net)
-    
+
     Parameters
     ----------
     X : pd.DataFrame
@@ -51,7 +42,7 @@ def train_en(X,y,n_jobs=16,cv=None):
         number of jobs to spawn
     cv : sklearn.model_selection.KFold
         cv object
-    
+
     Returns
     -------
     sklearn.linear_model.ElasticNet
@@ -61,35 +52,55 @@ def train_en(X,y,n_jobs=16,cv=None):
     list
         list with features used to train Layer 3
     """
-    preds = []
+    if not _has_sklearn:
+        raise ImportError(
+            "This function requires the optional dependency `scikit-learn`. Run `pip install "
+            "scikit-learn` and try again."
+        )
 
     model = ElasticNet()
     crossv_mod = clone(model)
     ret_mod = clone(model)
 
-    set_reg = [0.01,1.0,10.0,100.0,1000.0,10000.0,10000.0,100000.0,1000000.0,1000000000,1000000]
-    set_reg.extend([x/2 for x in set_reg])
-    set_reg.extend([x/3 for x in set_reg])
-    
+    set_reg = [
+        0.01,
+        1.0,
+        10.0,
+        100.0,
+        1000.0,
+        10000.0,
+        10000.0,
+        100000.0,
+        1000000.0,
+        1000000000,
+        1000000,
+    ]
+    set_reg.extend([x / 2 for x in set_reg])
+    set_reg.extend([x / 3 for x in set_reg])
+
     params = {
-       'alpha': set_reg,
-       'l1_ratio' : [0.01,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0],
-       'copy_X':[True],
-       'normalize' : [False],
-       'positive' : [True],
-       'fit_intercept'  : [True,False]
+        "alpha": set_reg,
+        "l1_ratio": [0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
+        "copy_X": [True],
+        "normalize": [False],
+        "positive": [True],
+        "fit_intercept": [True, False],
     }
 
-    grid = GridSearchCV(model, params,cv=cv,scoring='neg_mean_absolute_error',verbose=0,n_jobs=n_jobs,refit=True)
-    grid.fit(X,y)
-    
-    cv_pred = cv
-    crossv_mod.set_params(**grid.best_params_)
-    preds = cross_val_predict(crossv_mod, X=X, y=y, cv=cv_pred, n_jobs=n_jobs, verbose=0)
+    grid = GridSearchCV(
+        model,
+        params,
+        cv=cv,
+        scoring="neg_mean_absolute_error",
+        verbose=0,
+        n_jobs=n_jobs,
+        refit=True,
+    )
+    grid.fit(X, y)
 
+    crossv_mod.set_params(**grid.best_params_)
+    
     ret_mod.set_params(**grid.best_params_)
-    ret_mod.fit(X,y)
+    ret_mod.fit(X, y)
 
-    coef_indexes = [i for i,coef in enumerate(ret_mod.coef_) if coef > 0.0]
-   
-    return ret_mod
\ No newline at end of file
+    return ret_mod
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..1adaf18
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,65 @@
+[project]
+name = "deeplc"
+version = "2.2.27"
+description = "DeepLC: Retention time prediction for (modified) peptides using Deep Learning."
+readme = "README.md"
+license = { file = "LICENSE" }
+authors = [
+    { name = "Robbin Bouwmeester", email = "robbin.bouwmeester@ugent.be" },
+    { name = "Niels Hulstaert" },
+    { name = "Arthur Declercq" },
+    { name = "Ralf Gabriels" },
+    { name = "Lennart Martens" },
+    { name = "Sven Degroeve" },
+]
+classifiers = [
+    "Intended Audience :: Science/Research",
+    "License :: OSI Approved :: Apache Software License",
+    "Operating System :: OS Independent",
+    "Programming Language :: Python :: 3",
+    "Topic :: Scientific/Engineering :: Bio-Informatics",
+    "Development Status :: 5 - Production/Stable",
+]
+requires-python = ">=3.7"
+keywords = [
+    "DeepLC",
+    "Proteomics",
+    "deep learning",
+    "peptides",
+    "retention time",
+    "prediction",
+]
+
+dependencies = [
+    "tensorflow>=2.2,<2.13.0",
+    "numpy>=1.17,<2",
+    "pandas>=0.25,<2",
+    "h5py>=2.10.0,<4",
+    "pygam>=0.8.0,<1",
+    "deeplcretrainer>=0.1,<1",
+    "psm_utils>=0.2.3,<1",
+    "hdf5plugin>=4.1.1",
+]
+
+[project.optional-dependencies]
+test = ["pytest", "matplotlib>=3,<4"]
+gui = ["gooey>=1.0"]
+plot = ["plotly>=5"]
+deepcallc = ["scikit-learn<2,>=0.24.0"]
+
+[project.scripts]
+deeplc = "deeplc.__main__:main"
+deeplc-gui = "deeplc.gui:start_gui"
+
+[project.urls]
+GitHub = "https://github.com/compomics/deeplc"
+PyPi = "https://pypi.org/project/deeplc/"
+CompOmics = "https://www.compomics.com"
+
+[build-system]
+requires = ["setuptools"]
+build-backend = "setuptools.build_meta"
+
+[tool.setuptools]
+packages = ["deeplc"]
+include-package-data = true
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 312e33a..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,3 +0,0 @@
-[metadata]
-description-file = README.md
-license_files = LICENSE
diff --git a/setup.py b/setup.py
deleted file mode 100644
index ae9cc69..0000000
--- a/setup.py
+++ /dev/null
@@ -1,65 +0,0 @@
-from setuptools import find_packages, setup
-
-with open("README.md", "r") as fh:
-    LONG_DESCRIPTION = fh.read()
-
-
-setup(
-    name="deeplc",
-    version="2.2.27",
-    license="apache-2.0",
-    description="DeepLC: Retention time prediction for (modified) peptides using Deep Learning.",
-    long_description=LONG_DESCRIPTION,
-    long_description_content_type="text/markdown",
-    author="Robbin Bouwmeester, Niels Hulstaert, Arthur Declercq, Ralf Gabriels, Prof. Lennart Martens, Prof. Sven Degroeve",
-    author_email="Robbin.Bouwmeester@UGent.be",
-    url="http://compomics.github.io/projects/DeepLC",
-    project_urls={
-        "Documentation": "http://compomics.github.io/projects/DeepLC",
-        "Source": "https://github.com/compomics/DeepLC",
-        "Tracker": "https://github.com/compomics/DeepLC/issues",
-    },
-    packages=find_packages(),
-    include_package_data=True,
-    entry_points={
-        "console_scripts": [
-            "deeplc=deeplc.__main__:main",
-            "deeplc-gui=deeplc.gui:start_gui",
-        ]
-    },
-    keywords=[
-        "DeepLC",
-        "Proteomics",
-        "deep learning",
-        "peptides",
-        "retention time",
-        "prediction",
-    ],
-    classifiers=[
-        "Intended Audience :: Science/Research",
-        "License :: OSI Approved :: Apache Software License",
-        "Operating System :: OS Independent",
-        "Programming Language :: Python :: 3",
-        "Topic :: Scientific/Engineering :: Bio-Informatics",
-        "Development Status :: 4 - Beta",
-    ],
-    install_requires=[
-        "setuptools>=42.0.1",
-        "tensorflow>=2.2,<2.13.0",
-        "scipy>=1.4.1,<2",
-        "numpy>=1.17,<2",
-        "pandas>=0.25,<2",
-        "matplotlib>=3,<4",
-        "h5py>=2.10.0,<4",
-        "pygam>=0.8.0,<1",
-        "scikit-learn>=0.24.0,<2",
-        "deeplcretrainer>=0.1,<1",
-        "psm_utils>=0.2.3,<1",
-        "hdf5plugin>=4.1.1",
-    ],
-    extras_require={
-        "gui": ["gooey>=1.0"],
-        "plot": ["plotly>=5"],
-    },
-    python_requires=">=3.7",
-)
diff --git a/tests/test_deeplc.py b/tests/test_deeplc.py
index e88d9b9..4a5af2f 100644
--- a/tests/test_deeplc.py
+++ b/tests/test_deeplc.py
@@ -1,39 +1,52 @@
 "Unit and integration tests for DeepLC."
 
-# Standard library
 import logging
-import pytest
 import subprocess
 
-# Third party
+import numpy as np
 import pandas as pd
-from sklearn.metrics import r2_score
+import pytest
 
-# DeepLC
 import deeplc
 
 
+def _r2_score(y_true, y_pred):
+    y_true = np.array(y_true)
+    y_pred = np.array(y_pred)
+    numerator = ((y_true - y_pred) ** 2).sum(dtype=np.float64)
+    denominator = ((y_true - np.average(y_true)) ** 2).sum(dtype=np.float64)
+    if denominator == 0.0:
+        return 1.0
+    return 1 - numerator / denominator
+
+
 def test_cli_basic():
-    """ Test command line interface help message. """
-    assert subprocess.getstatusoutput('deeplc -h')[0] == 0, "`deeplc -h` \
-returned non-zero status code"
+    """Test command line interface help message."""
+    assert (
+        subprocess.getstatusoutput("deeplc -h")[0] == 0
+    ), "`deeplc -h` returned non-zero status code"
 
 
 def test_cli_full():
-    """" Test command line interface with input files."""
+    """ " Test command line interface with input files."""
     file_path_pred = "examples/datasets/test_train.csv"
     file_path_cal = "examples/datasets/test_train.csv"
     file_path_out = "pytest_cli_out.csv"
 
     command = [
-        "deeplc", "--file_pred", file_path_pred, "--file_cal", file_path_cal,
-        "--file_pred_out", file_path_out,
+        "deeplc",
+        "--file_pred",
+        file_path_pred,
+        "--file_cal",
+        file_path_cal,
+        "--file_pred_out",
+        file_path_out,
     ]
     subprocess.run(command, check=True)
 
     preds_df = pd.read_csv(file_path_out)
     train_df = pd.read_csv(file_path_pred)
-    model_r2 = r2_score(train_df['tr'], preds_df['predicted retention time'])
+    model_r2 = _r2_score(train_df["tr"], preds_df["predicted retention time"])
     logging.info("DeepLC R2 score on %s: %f", file_path_pred, model_r2)
     assert model_r2 > 0.90, f"DeepLC R2 score on {file_path_pred} below 0.9 \
 (was {model_r2})"