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. 2019 Mar 19;24(6):1081.
doi: 10.3390/molecules24061081.

CuDDI: A CUDA-Based Application for Extracting Drug-Drug Interaction Related Substance Terms from PubMed Literature

Yin Lu  1 , Aditya Chandra Vothgod Ramachandra  2 , Minh Pham  3 , Yi-Cheng Tu  4 , Feng Cheng  5   6
Affiliations

CuDDI: A CUDA-Based Application for Extracting Drug-Drug Interaction Related Substance Terms from PubMed Literature

Yin Lu et al. Molecules. .

Abstract

Drug-drug interaction (DDI) is becoming a serious issue in clinical pharmacy as the use of multiple medications is more common. The PubMed database is one of the biggest literature resources for DDI studies. It contains over 150,000 journal articles related to DDI and is still expanding at a rapid pace. The extraction of DDI-related information, including compounds and proteins from PubMed, is an essential step for DDI research. In this paper, we introduce a tool, CuDDI (compute unified device architecture-based DDI searching), for identification of DDI-related terms (including compounds and proteins) from PubMed. There are three modules in this application, including the automatic retrieval of substances from PubMed, the identification of DDI-related terms, and the display of relationship of DDI-related terms. For DDI term identification, a speedup of 30⁻105 times was observed for the compute unified device architecture (CUDA)-based version compared with the implementation with a CPU-based Python version. CuDDI can be used to discover DDI-related terms and relationships of these terms, which has the potential to help clinicians and pharmacists better understand the mechanism of DDIs. CuDDI is available at: https://github.com/chengusf/CuDDI.

Keywords: CUDA; GPU; PubMed; Substance; drug-drug interaction; mechanism; parallel computing; random sampling; term.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
The social network of the proteins and FDA approved drugs identified from CuDDI.
Figure 2
Figure 2
(A) The comparison of the speedup of the compute unified device architecture (CUDA) vs. Python code for different threads across six drugs. (B) The comparison of sample size for six drugs.
Figure 3
Figure 3
The flowchart of the process in CuDDI.
See this image and copyright information in PMC

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