MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)
Abstract
Matching molecular analogues is a computational chemistry and bioinformatics research issue which is used to identify molecules that are structurally or functionally similar to a target molecule. Recent studies on matching analogous molecules have predominantly concentrated on enhancing effectiveness, often sidelining computational efficiency, particularly in contexts of low computational resources. This oversight poses challenges in many real applications (e.g., drug discovery, catalyst generation and so forth). To tackle this issue, we propose a general strategy named MapLE, aiming to promptly match analogous molecules with low computational resources by multi-metrics evaluation. Experimental evaluation conducted on a public biomolecular dataset validates the excellent and efficient performance of the proposed strategy.
Cite
Text
Chen et al. "MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30427Markdown
[Chen et al. "MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/chen2024aaai-maple/) doi:10.1609/AAAI.V38I21.30427BibTeX
@inproceedings{chen2024aaai-maple,
title = {{MapLE: Matching Molecular Analogues Promptly with Low Computational Resources by Multi-Metrics Evaluation (Student Abstract)}},
author = {Chen, Xiaojian and Liao, Chuyue and Gu, Yanhui and Li, Yafei and Wang, Jinlan and Chen, Yi and Kitsuregawa, Masaru},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23456-23457},
doi = {10.1609/AAAI.V38I21.30427},
url = {https://mlanthology.org/aaai/2024/chen2024aaai-maple/}
}