Metadata-Version: 2.1
Name: carate
Version: 0.1.1
Summary: Graph-based encoder algorithm
Home-page: http://www.codeberg/sail.black/serial-sphinx
Author: Julian M. Kleber
Author-email: julian.kleber@sail.black
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: black
Requires-Dist: Click

# CARATE
[![License: AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
![Python Versions](https://img.shields.io/badge/python-3.9%20%7C%203.10%20%7C%20-blue) 
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# Why 

Molecular representation is wrecked. Seriously! We chemists talk with an ancient language about something we can't comprehend with that language for decades. It has to stop. 

# What 

The success of transformer models is evident. Applied to molecules we need a graph-based transformer. Such models can then learn hidden representations of a molecule better suited to describe a molecule. 

For a chemist it is quite intuitive but seldomly modelled as such: A molecule exhibits properties through its combined *electronic and structural features*

* Evidence of this perspective  was given in [chembee](https://codeberg.org/sail.black/chembee.git). 

* Mathematical equivalence of the variational principle and neural networks was given in the thesis [Markov-chain modelling of dynmaic interation patterns in supramolecular complexes](https://www.researchgate.net/publication/360107521_Markov-chain_modelling_of_dynamic_interaction_patterns_in_supramolecular_complexes). 

* The failure of the BOA is described in the case of diatomic tranistion metal fluorides is described in a [Can Fluorine form triple bonds?](https://chemrxiv.org/engage/chemrxiv/article-details/620f745121686706d17ac316)

* Evidence of quantum-mechanical simulations via molecular dynamics is given in a seminal work [Direct Simulation of Bose-Einstein-Condensates using molecular dynmaics and the Lennard-Jones potential](https://www.researchgate.net/publication/360560870_Direct_simulation_of_Bose-Einstein_condesates_using_molecular_dynamics_and_the_Lennard-Jones_potential)
# Scope

The aim is to implement the algorithm in a reusable way, e.g. for the [chembee](https://codeberg.org/sail.black/chembee.git) pattern. Actually, the chembee pattern is mimicked in this project to provide a stand alone tool. The overall structure of the program is reusable for other deep-learning projects and will be transferred to an own project that should work similar to opinionated frameworks. 

# Installation on CPU 

Prepare system 
```bash
sudo apt-get install python3-dev libffi-dev
```

```bash 
pip3 install torch torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cpu 
pip install torch-scatter torch-sparse torch-geometric rdkit-pypi networkx[default] matplotlib
pip install torch-cluster 
pip install torch-spline-conv 
``` 

# Outlook 

The program is 

# Cite 

There is a preprint available on bioRxiv. Read the [preprint](https://www.biorxiv.org/content/10.1101/2022.02.12.470636v1)
