MolEval: An Evaluation Toolkit for Molecular Embeddings via LLMs
Abstract
Inspired by SentEval and MTEB for sentence embeddings and DeepChem for molecular machine learning, we introduce MolEval. MolEval tackles the issue of evaluating large language models (LLMs) embeddings, which are traditionally expensive to execute on standard computing hardware. It achieves this by offering a repository of pre-computed molecule embeddings alongside a versatile platform that facilitates the evaluation of any embeddings derived from molecular structures. This approach not only streamlines the assessment process but also makes it more accessible to researchers and practitioners in the field.
Cite
Text
Sadeghi et al. "MolEval: An Evaluation Toolkit for Molecular Embeddings via LLMs." ICML 2024 Workshops: AccMLBio, 2024.Markdown
[Sadeghi et al. "MolEval: An Evaluation Toolkit for Molecular Embeddings via LLMs." ICML 2024 Workshops: AccMLBio, 2024.](https://mlanthology.org/icmlw/2024/sadeghi2024icmlw-moleval/)BibTeX
@inproceedings{sadeghi2024icmlw-moleval,
title = {{MolEval: An Evaluation Toolkit for Molecular Embeddings via LLMs}},
author = {Sadeghi, Shaghayegh and Forooghi, Ali and Lu, Jianguo and Ngom, Alioune},
booktitle = {ICML 2024 Workshops: AccMLBio},
year = {2024},
url = {https://mlanthology.org/icmlw/2024/sadeghi2024icmlw-moleval/}
}