Datasets:
license: mit
task_categories:
- text-generation
- translation
tags:
- chemistry
- biology
ChEBI-20-MM Dataset
Overview
The ChEBI-20-MM is an extensive and multi-modal benchmark developed from the ChEBI-20 dataset. It is designed to provide a comprehensive benchmark for evaluating various models' capabilities in the field of molecular science. This benchmark integrates multi-modal data, including InChI, IUPAC, SELFIES, and images, making it a versatile tool for a wide range of molecular tasks.
Dataset Description
ChEBI-20-MM is an expansion of the original ChEBI-20 dataset, with a focus on incorporating diverse modalities of molecular data. This benchmark is tailored to assess models in several key areas:
- Molecule Generation: Evaluating the ability of models to generate accurate molecular structures.
- Image and IUPAC Recognition: Testing models on their proficiency in interpreting and converting molecular images and IUPAC names into other representational formats.
- Molecular Captioning: Assessing the capability of models to generate descriptive captions for molecular structures.
- Retrieval Tasks: Measuring the effectiveness of models in retrieving molecular information accurately and efficiently.
Utility and Significance
By expanding the data modality variety, this benchmark enables a more comprehensive evaluation of models' performance in multi-modal data handling. It provides an opportunity to explore the intersection of molecular science and advanced computational models.
How to Use
Model reviews and evaluations related to this dataset can be directly accessed and used via the LLM4Mol link: LLM4Mol.
Acknowledgments
The development of the ChEBI-20-MM dataset was inspired by the ChEBI-20 in molecule generation and captioning initiated by MolT5. Additional data information supplements are derived from PubChem.