Metadata-Version: 2.4
Name: qsarmil
Version: 1.0.3
Summary: Molecular multi-instance machine learning
Author-email: Dmitry Zankov <dvzankov@gmail.com>
License: MIT
Description-Content-Type: text/x-rst
License-File: LICENSE
Requires-Dist: psutil
Requires-Dist: rdkit
Requires-Dist: molfeat
Requires-Dist: mikit-learn
Requires-Dist: qsarcons
Requires-Dist: py3Dmol
Dynamic: license-file


QSARmil - molecular multi-instance machine learning
============================================================
``QSARmil`` is a package for designing pipelines for building QSAR models with multi-instance machine learning algorithms.

Introduction
--------------------------
Multi-instance machine learning for molecules

Installation
--------------------------

.. code-block:: bash

    pip install qsarmil

Benchmarking QSARmil
--------------------------

To facilitate benchmarking ``QSARmil`` against alternative platforms, we developed a meta-model builder that eliminates
the need for manual adjustments to the model-building protocol. The pipeline automatically generates multiple
multi-conformer models using diverse descriptor sets and multi-instance learning methods, and then applies
a genetic algorithm to identify the optimal consensus combination of individual models. The input data should be provided
as a pandas DataFrame, where the first column contains the molecular SMILES strings and the second column contains
the corresponding target property values.

.. code-block:: python

     from qsarmil.meta import MultiConformerModel

     model = MultiConformerModel(num_conf=10, hopt=True, task="regression", output_folder="mcf", verbose=True)
     y_pred = model.run_predict(df_train, df_test)

Use cases
--------------------------

See the examples of ``QSARmil`` application for different tasks in the `tutorial collection <notebooks>`_ .
