metadata

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 Recognition: Testing models on their proficiency in converting molecular images into other representational formats.
IUPAC Recognition: Evaluating the ability of models to generate IUPAC names from 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.

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.