From edb150b7f71e75af3070271a69a1287359d41ffc Mon Sep 17 00:00:00 2001 From: Kohulan Rajan Date: Mon, 14 Oct 2024 17:31:40 +0200 Subject: [PATCH] feat: include training information README.md --- README.md | 66 +++++++++++++++++++++++++++++++++++-------------------- 1 file changed, 42 insertions(+), 24 deletions(-) diff --git a/README.md b/README.md index 5f7de73..0497ef2 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@
STOUT Logo
-V2.0 + STOUT V2.0
@@ -47,6 +47,7 @@ V2.0 Key FeaturesInstallationHow To Use • + Training STOUTAcknowledgementsCitation

@@ -57,33 +58,41 @@ V2.0 ## Key Features - +- 🧪 Translate SMILES to IUPAC names +- 🔬 Convert IUPAC names back to valid SMILES strings +- 🤖 Powered by advanced transformer models +- 💻 Cross-platform support (Linux, macOS, Windows via Ubuntu shell) +- 🚀 High-performance chemical nomenclature translation +- 🧠 Training code available for custom model development ## Installation -

Choose your preferred installation method:

+Choose your preferred installation method:
📦 PyPI Installation -
pip install STOUT-pypi
+ +```bash +pip install STOUT-pypi +```
🐍 Conda Environment Setup -
conda create --name STOUT python=3.10 
+
+```bash
+conda create --name STOUT python=3.10 
 conda activate STOUT
-conda install -c decimer stout-pypi
+conda install -c decimer stout-pypi +```
📥 Direct Repository Installation -
pip install git+https://github.com/Kohulan/Smiles-TO-iUpac-Translator.git
+ +```bash +pip install git+https://github.com/Kohulan/Smiles-TO-iUpac-Translator.git +```
## How To Use @@ -102,6 +111,23 @@ SMILES = translate_reverse(IUPAC_name) print(f"🔬 SMILES of {IUPAC_name} is: {SMILES}") ``` +## Training STOUT + +For researchers interested in training their own STOUT models or understanding the training process, we provide the training code in a separate repository: + +[STOUT Training Repository](https://github.com/Kohulan/IWOMI_Tutorials/tree/IWOMI_2024/STOUT_Training) + +This repository contains the necessary scripts and instructions for training STOUT models. Please note that training requires significant computational resources and a large dataset. Refer to the README in the training repository for detailed instructions. + +## Model Card + +> Rajan, K., Steinbeck, C., & Zielesny, A. (2024). STOUT V2 - Model library (Version v3). Zenodo. https://doi.org/10.5281/zenodo.13318286 + +### Model Use +- Primary intended uses: Translation between SMILES and IUPAC names for chemical compounds +- Primary intended users: Chemists, researchers, and developers in the field of cheminformatics +- Out-of-scope use cases: Not intended for critical applications where 100% accuracy is required + ## Acknowledgements

@@ -128,25 +154,17 @@ print(f"🔬 SMILES of {IUPAC_name} is: {SMILES}") ## Citation -

1. Rajan, K., Zielesny, A. & Steinbeck, C. STOUT: SMILES to IUPAC names using neural machine translation. J Cheminform 13, 34 (2021). https://doi.org/10.1186/s13321-021-00512-4 -
-
-2. Rajan K, Zielesny A, Steinbeck C. STOUT V2.0: SMILES to IUPAC name conversion using transformer models. ChemRxiv. 2024; https://doi.org/10.26434/chemrxiv-2024-089vs -
-## Model Card -
-Rajan, K., Steinbeck, C., & Zielesny, A. (2024). STOUT V2 - Model library. Zenodo. https://doi.org/10.5281/zenodo.13318286 -
+2. Rajan K, Zielesny A, Steinbeck C. STOUT V2.0: SMILES to IUPAC name conversion using transformer models. ChemRxiv. 2024; https://doi.org/10.26434/chemrxiv-2024-089vs -

Repository Analytics

+## Repository Analytics

Repobeats analytics image

-
+---

Made with ❤️ by the Steinbeck Group