license: cc-by-4.0
task_categories:
- text-generation
- feature-extraction
language:
- en
tags:
- chemistry
- chemoinformatics
- reaction-prediction
- RAG
pretty_name: ORDerly Styrene Mizoroki-Heck RAG Dataset
size_categories:
- 10K<n<100K
ORDerly: Styrene Mizoroki-Heck RAG-Ready Dataset
This repository contains chemical reaction data formatted for Retrieval-Augmented Generation (RAG) systems. The data is a processed version of the ORDerly benchmark, specifically focusing on reaction conditions and forward/retro prediction tasks.
Dataset Structure
The data is split into 10,000-row Parquet chunks to prevent Out-of-Memory (OOM) errors during ingestion into vector databases. It includes:
- orderly_condition: Solvents and agents given reactants and products.
- orderly_forward: Product prediction given reactants and agents.
- orderly_retro: Retrosynthesis planning.
How to Use
from datasets import load_dataset
dataset = load_dataset("Azzindani/Open_Reaction_Data", streaming=True)
Citations and Data Source
This dataset is based on the ORDerly framework. If you use this data, please cite the original authors:
Paper:
Wigh, D. S., et al. (2024). ORDerly: Data Sets and Benchmarks for Chemical Reaction Data. Journal of Chemical Information and Modeling. https://doi.org/10.1021/acs.jcim.4c00292
Original Dataset:
Wigh, D. S., et al. (2023). ORDerly chemical reactions condition benchmarks. Figshare. https://doi.org/10.6084/m9.figshare.23298467.v4
License
Licensed under CC BY 4.0.