Metadata-Version: 2.1
Name: twinbooster
Version: 0.2.4
Summary: Python package for TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction
Author-email: "Maximilian G. Schuh" <m.schuh@tum.de>
License: MIT License
        
        Copyright (c) 2024 Maximilian Schuh
        
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Project-URL: Repository, https://github.com/maxischuh/TwinBooster
Requires-Python: ==3.8.*
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: torch==2.0.1
Requires-Dist: transformers==4.30.2
Requires-Dist: datasets
Requires-Dist: tqdm
Requires-Dist: scikit-learn
Requires-Dist: scipy
Requires-Dist: joblib
Requires-Dist: matplotlib
Requires-Dist: lightgbm==3.3.5
Requires-Dist: rdkit==2023.3.2
Requires-Dist: pynvml
Requires-Dist: ConfigSpace
Requires-Dist: smac
Requires-Dist: optuna
Requires-Dist: jupyterlab
Requires-Dist: pathlib

# Python package for TwinBooster

[![arXiv](https://img.shields.io/badge/arXiv-2401.04478-b31b1b.svg)](https://arxiv.org/abs/2401.04478)
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/maxischuh/TwinBooster/blob/main/twinbooster/twinbooster_example.ipynb)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
![Python version](https://img.shields.io/badge/python-v.3.8-blue)
![License](https://img.shields.io/badge/license-MIT-orange)

### Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction

Maximilian G. Schuh, Davide Boldini, Stephan A. Sieber

@ Chair of Organic Chemistry II,
TUM School of Natural Sciences,
Technical University of Munich

**Abstract**

The success of drug discovery and development relies on the precise prediction of molecular activities and properties. While in silico molecular property prediction has shown remarkable potential, its use has been limited so far to assays for which large amounts of data are available. In this study, we use a fine-tuned large language model to integrate biological assays based on their textual information, coupled with Barlow Twins, a Siamese neural network using a novel self-supervised learning approach. This architecture uses both assay information and molecular fingerprints to extract the true molecular information. TwinBooster enables the prediction of properties of unseen bioassays and molecules by providing state-of-the-art zero-shot learning tasks. Remarkably, our artificial intelligence pipeline shows excellent performance on the FS-Mol benchmark. This breakthrough demonstrates the application of deep learning to critical property prediction tasks where data is typically scarce. By accelerating the early identification of active molecules in drug discovery and development, this method has the potential to help streamline the identification of novel therapeutics.

An example script can be found here ```./twinbooster/twinbooster_example.ipynb```.

_More coming soon_
