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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: 05 April 2024 Publication History

Abstract

The selection of drug targets is a key link in drug design. The correct selection of targets depends on biomolecular activity, which refers to the ability or property of interaction between biomolecules (such as proteins, enzymes, receptors, etc.) and other molecules (such as drugs, compounds). It describes the effects and reactions of biomolecules on other molecules within an organism or in a laboratory environment. Biomolecular activity can include multiple aspects of properties and effects, such as: binding activity, enzymatic activity, activation or inhibition activity, cell action activity. Biomolecular activity refers to the binding activity of molecules in drug design, so the selection of appropriate targets needs to predict the biological activity of molecular proteins (the binding activity of molecules). In this paper, we present an FPGA hardware accelerator for predicting molecular protein activity prediction, which deploies lasso and deep neural network (SED) algorithms for screening extended connectivity fingerprints. In order to speed up the algorithm to process data more efficiently, it used the minimum integer bit width and decimal point width to reduce the hardware processing of data under the condition of reasonable accuracy loss, and used AXI MASTER interface to improve the bandwidth of the algorithm to transfer data from memory. Experimental results show that our FPGA accelerator implemented on Xilinx Zynq UltraScale+ XCZU7EV is 4.3 times faster than the algorithm implemented on Intel i7-7700K [email protected].

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ISAIMS '23: Proceedings of the 2023 4th International Symposium on Artificial Intelligence for Medicine Science
October 2023
1394 pages
ISBN:9798400708138
DOI:10.1145/3644116
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

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Published: 05 April 2024

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