Initial commit of repository

LICENSE

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+MIT License
+
+Copyright (c) 2022 AlexanderKroll
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# Description
+This repository contains an easy-to-use Python function for the kcat prediction model from our paper "Enzymatic turnover numbers: A general prediction model allows full kinetic parameterization of genome-scale metabolic models". 
+
+## Predicting kcat values for enzyme-reaction pairs
+The kcat prediction model was only trained with natural enzyme-reaction pairs with wild-type enzymes. Hence, the model will not be good at predicting kcat for mutants
+or for non-natural reactions of enzymes. 
+
+## Downloading data folder
+Before you can run the kcat prediction function, you need to download and unzip a [data folder](https://drive.google.com/file/d/1rH9mQ4dL1SPeD_ttAVxrKrUl-7z4FMEK/view?usp=sharing). Afterwards, this repository should have the following strcuture:
+
+    ├── code                   
+    ├── data                    
+    └── README.md
+
+## substrate and product representations
+You can use InChI strings, KEGG Compound IDs, and SMILES strings as substrate/product representations.
+
+## Requirements
+
+- python 3.7
+- jupyter
+- pandas 1.1.3
+- torch 1.11.0
+- numpy 
+- rdkit 2020.09.1
+- fair-esm 0.4.0
+- py-xgboost 1.6.1
+
+The listed packages can be installed using conda and anaconda:
+
+```bash
+pip install pandas==1.1.3
+pip install torch==1.11.0
+pip install numpy
+pip install fair-esm==0.4.0
+conda install -c conda-forge py-xgboost=1.6.1
+conda install -c rdkit rdkit=2020.09.1
+```
+
+## Content
+
+There is a Jupyter notebook "Tutorial kcat prediction.ipynb" in the folder "code" that contains an example on how to use the kcat prediction function.
+
+## Problems/Questions
+If you face any issues or problems, please open an issue.
+