0.4.0 alpha version of the package that can be pip installed

README.md

 SENSE-PPI
 =======================================
 
-SENSE-PPI is a Deep Learning model for predicting physical protein-protein interactions based on amino acid sequences.
+SENSE-PPI is a Deep Learning model for predicting physical protein-protein interactions based on amino acid sequences. 
+It is based on embeddings generated by ESM2 and uses Siamese RNN architecture to perform a binary classification.
 
-P.S.: Both pretrained models were made to work with protein in range 50-800 amino acids.
+## Installation
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+
+SENSE-PPI requires Python 3.10 or higher. To install the package, run:
+
+```bash
+pip install senseppi
+```
+
+**N.B.**: if you intend to use the `create_dataset` command to generate new datasets from STRING,
+do not forget to additionally install the MMseqs2 software (instructions can be found at: https://github.com/soedinglab/MMseqs2).
+The `mmseqs` command should be available in your PATH.
+
+## Usage
+
+There are 5 commands available in the package:
+
+- `train`: trains SENSE-PPI on a given dataset
+- `test`: computes test metrics (AUROC, AUPRC, F1, MCC, Presicion, Recall, Accuracy) on a given dataset
+- `predict`: predicts interactions for a given dataset
+- `predict_string`: predicts interactions for a given dataset using STRING database:
+the interactions are taken from the STRING database (based on seed proteins). 
+Predictions are compared with the STRING database. Optionally, the graphs can be constructed.
+- `create_dataset`: creates a dataset from the STRING database based on the taxonomic ID of the organism.
+
+
+The original SENSE-PPI repository contains two pretrained models: `senseppi.ckpt` and `dscript.ckpt` pretrained on SENSE-PPI and DSCRIPT human datasets respectively.
+
+- `senseppi.ckpt` (preferred) : Download from [here](http://gitlab.lcqb.upmc.fr/Konstvv/SENSE-PPI/raw/master/pretrained_models/senseppi.ckpt)
+- `dscript.ckpt` : Download from [here](http://gitlab.lcqb.upmc.fr/Konstvv/SENSE-PPI/raw/master/pretrained_models/dscript.ckpt)
+
+**N.B.**: Both pretrained models were made to work with proteins in range 50-800 amino acids.
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requirements.txt

+numpy
+pandas
+wget
+scipy
+networkx
+torch>=1.12
+matplotlib
+seaborn
+tqdm
+scikit-learn
+pytorch-lightning==1.9.0
+torchmetrics
+biopython
+fair-esm
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senseppi/__init__.py

-__version__ = "0.3.2"
+__version__ = "0.4.0"
 __author__ = "Konstantin Volzhenin"
 
 from . import model, commands, esm2_model, dataset, utils, network_utils

setup.py

@@ -5,9 +5,9 @@ with open("README.md", "r") as fh:
     long_description = fh.read()
 
 setup(
-    name="dscript_data",
+    name="senseppi",
     version=senseppi.__version__,
-    description="SENSE_PPI: Sequence-based EvolutIoNary ScalE Protein-Protein Interaction prediction",
+    description="SENSE_PPI: Sequence-based EvolutioNary ScalE Protein-Protein Interaction prediction",
     author="Konstantin Volzhenin",
     author_email="konstantin.volzhenin@sorbonne-universite.fr",
     url="",
@@ -17,6 +17,17 @@ setup(
     long_description=long_description,
     long_description_content_type="text/markdown",
     include_package_data=True,
+    classifiers=[
+        "Programming Language :: Python :: 3",
+        "Intended Audience :: Science/Research",
+        "Operating System :: OS Independent",
+    ],
+    python_requires='>=3.9',
+    entry_points={
+        'console_scripts': [
+            'senseppi=senseppi.__main__:main',
+        ],
+    },
     install_requires=[
         "numpy",
         "pandas",
@@ -31,7 +42,6 @@ setup(
         "pytorch-lightning==1.9.0",
         "torchmetrics",
         "biopython",
-        "fair-esm",
-        "mmseqs2"
+        "fair-esm"
     ],
 )
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