Molecular Classification Using Hyperdimensional Graph Classification
<|reference_start|>Molecular Classification Using Hyperdimensional Graph Classification: Our work introduces an innovative approach to graph learning by leveraging Hyperdimensional Computing. Graphs serve as a widely embraced method for conveying information, and their utilization in learning has gained significant attention. This is notable in the field of chemoinformatics, where learning from graph representations plays a pivotal role. An important application within this domain involves the identification of cancerous cells across diverse molecular structures.   We propose an HDC-based model that demonstrates comparable Area Under the Curve results when compared to state-of-the-art models like Graph Neural Networks (GNNs) or the Weisfieler-Lehman graph kernel (WL). Moreover, it outperforms previously proposed hyperdimensional computing graph learning methods. Furthermore, it achieves noteworthy speed enhancements, boasting a 40x acceleration in the training phase and a 15x improvement in inference time compared to GNN and WL models. This not only underscores the efficacy of the HDC-based method, but also highlights its potential for expedited and resource-efficient graph learning.<|reference_end|>
@article{verges2024molecular,
    title={Molecular Classification Using Hyperdimensional Graph Classification},
    author={Pere Verges, Igor Nunes, Mike Heddes, Tony Givargis and Alexandru
  Nicolau},
    journal={arXiv preprint arXiv:2403.12307},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.12307},
    primaryClass={cs.LG cs.AI cs.NE q-bio.QM}
}