Metadata-Version: 2.1
Name: olorenchemengine
Version: 0.0.0
Summary: Add a short description here!
Home-page: https://github.com/pyscaffold/pyscaffold/
Author: Oloren AI
Author-email: dhuang26@gmail.com
Project-URL: Documentation, https://pyscaffold.org/
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python
Description-Content-Type: text/x-rst; charset=UTF-8
Provides-Extra: testing
License-File: LICENSE.txt

=============================
|oceLogo| Oloren ChemEngine
=============================

.. |oceLogo| image:: assets/oce_logo.png
  :height: 30

Oloren ChemEngine (oce) is a software package developed and maintained by Oloren AI containing a
unified API for the development and use of molecular property predictors enabling

* Direct development of high-performing predictors
* Integration of predictors into model interpretability, uncertainty quantification, and analysis frameworks

Here's an example of what we mean by this. In less than ten lines of code, we'll
train, save, load, and predict with a gradient-boosted model with two different
molecular vector representations.

.. code-block:: python

    import olorenchemengine as oce

    df = oce.ExampleDataFrame()
    
    model = oce.BaseBoosting([
                oce.RandomForestModel(oce.DescriptastorusDescriptor("rdkit2dnormalized"), n_estimators=1000),
                oce.RandomForestModel(oce.OlorenCheckpoint("default"), n_estimators=1000)])
    model.fit(df["Smiles"], df["pChEMBL Value"])
    
    oce.save(model, "model.oce")
    
    model2 = oce.load("model.oce")
    
    y_pred = model2.predict(["CC(=O)OC1=CC=CC=C1C(=O)O"])

It's that simple! And it's just as simple to train a graph neural network, generate
visualizations, and create error models. More information on features and
capabilities is available in our documentation at `docs.oloren.ai <https://docs.oloren.ai>`_.

-------------------------------
oce at a high level
-------------------------------

Everything in oce is built around Oloren's ``BaseClass`` system, which all classes stem from.
Any ``BaseClass`` derived objects has its parameters and complete state saved
via ``parmeterize`` and ``saves`` respectively. A blank object (no internal state)
can be recreated via ``create_BC`` and a complete object (with internal state) can
be recreated via ``loads``.

The system includes abstract subclasses of ``BaseClass`` are named ``Base{Class Type}``
and their interactions, most prominently

    * ``BaseModel``, a base class for all any molecular property predictor
    * ``BaseRepresentation``, a base class for all molecular representations
    * ``BaseVisualization``, a base class for all types of visualizations and analyses

This abstraction system is provided free of charge by Oloren AI in the internals.

-------------------------------
Getting Started with oce
-------------------------------
_______________________________
Installation
_______________________________

After cloning the repository, you can install the package with the following command:

.. code-block:: bash

    bash install.sh
    pip install . # add -e for development

This will work fine in both a conda environment and a pip environment.

_______________________________
Basic Usage
_______________________________
We have an examples folder, which we'd highly reccomend you checkout--1A and 1B
in particular--the rest of the examples can be purused when the topics come up.

-------------------------------
Our Thanks
-------------------------------
First, our thanks to the community of developers and scientists, who've built and maintained
a repotoire of software libraries and scripts which have been invaluable. We'd like
to particularly thank the folks creating RDKit, PyTorch Geometric, and SKLearn who've
developed software we strive to emulate and exceed.

Second, we'd like to thank the amazing developers at Oloren who've created Oloren
ChemEngine through enoromous effort and dedication. And, we'd like to thank our future
collaborators and contributors ahead, who we're excited meet and work with.

Third, huge gratitude goes to our investors, clients, and customers who've been
ever patient and ever gracious, who've provided us with the opportunity to bring
something we believe to be truly valuable into the world.
