Hardware acceleration of the SED algorithm for Biomolecular activity predictionBiomolecular activity algorithm (SED) uses FPGA parallel programmability to achieve hardware acceleration-Reference-Cited by-同舟云学术

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Hardware acceleration of the SED algorithm for Biomolecular activity predictionBiomolecular activity algorithm (SED) uses FPGA parallel programmability to achieve hardware acceleration

Published:2023-10-20 Issue: Volume:12 Page:1136-1140
ISSN:
Container-title:Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science
language:
Short-container-title:

Author:

Kang Zhengdong¹^ORCID

Affiliation:

1. College of Physics and Information Engineering, Fuzhou University, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3644116.3644309

Reference11 articles.

1. Gonzalo C, Manuel J C, Nicolás G. Graph-Based Feature Selection Approach for Molecular Activity Prediction. [J]. Journal of chemical information and modeling, 2022, 62(7).

2. K J W, A J M, Sugato B, Simplified, interpretable graph convolutional neural networks for small molecule activity prediction. [J]. Journal of computer-aided molecular design, 2021, 36(5).

3. Gonzalo C, José T P, Aída H D, Influence of feature rankers in the construction of molecular activity prediction models. [J]. Journal of computer-aided molecular design, 2020, 34(3).

4. Li X, Fourches D. Inductive transfer learning for molecular activity prediction: Next - Gen QSAR Models with MolPMoFiT [J]. Journal of Cheminformatics, 2020, 12(1).

5. Lowell D, Dewayne S, Felix O. Cellular and Molecular Activities of IP6 in Disease Prevention and Therapy. [J]. Biomolecules, 2023, 13(6).

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