carate 0.1.1

Graph-based encoder algorithm

CARATE

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.

• Mathematical equivalence of the variational principle and neural networks was given in the thesis Markov-chain modelling of dynmaic interation 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?

• 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

Scope

The aim is to implement the algorithm in a reusable way, e.g. for the chembee 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

sudo apt-get install python3-dev libffi-dev

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