Parallel Graph Database for Rapid Drug Repurposing
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Solution Overview
Problem
The conventional drug repurposing process is cumbersome and time-consuming, requiring manual handling of vast amounts of multi-modal data and complex computations to identify potential therapeutic drugs for diseases, often taking months to generate insights, whereas there is a need for a rapid response, especially during pandemics.
Innovation Solution
A massively parallel graph database technology is employed to process multi-modal data represented in knowledge graphs, enabling interactive query and semantic-traversal capabilities, accelerating domain-specific functions like protein similarity analysis to generate drug hypotheses within seconds, using a scalable graph database like the Cray Graph Engine for rapid-response drug repurposing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual handling of multi-modal data is used for drug repurposing, then comprehensive analysis can be performed, but the process becomes time-consuming and cumbersome
Solution Approach 1:
The patent replaces manual mechanical handling of multi-modal data with automated graph database technology. The graph engine automatically ingests, processes, and queries diverse data types (genomic, proteomic, clinical, literature) without manual intervention, maintaining comprehensive analysis capabilities while eliminating the time-consuming manual operations. This substitution of automated computational systems for manual processes directly resolves the contradiction between analysis comprehensiveness and time consumption.
Solution Approach 2:
The patent transforms the data processing approach by changing the fundamental parameters of how multi-modal data is handled. Instead of sequential manual analysis, the system uses parallel graph query processing with optimized traversal algorithms. The graph database structure enables simultaneous processing of multiple data modalities through efficient join operations and semantic traversal, fundamentally changing the time complexity from linear/manual to parallel/computational, thereby reducing drug repurposing time while preserving analytical depth.
2Measurement precision
If vast amounts of multi-modal data are processed to generate drug hypotheses, then insight quality improves, but computational complexity increases
Solution Approach 1:
The patent segments the complex computational task into distinct modular components: data ingestion modules for different modalities, graph construction modules, query processing modules, and hypothesis generation modules. Each segment handles specific aspects of data processing independently, allowing the system to manage vast multi-modal data without overwhelming computational complexity. The segmentation enables parallel processing and simplifies the overall computational architecture while maintaining high insight quality through comprehensive data integration.
Solution Approach 2:
The patent introduces a graph database as an intermediary structure between raw multi-modal data and final drug hypotheses. This intermediary graph representation transforms complex multi-modal relationships into a unified semantic model where entities and relationships are explicitly modeled. The graph structure serves as a computational mediator that simplifies complex queries and enables efficient traversal across diverse data types, reducing the apparent computational complexity while preserving the quality of insights through structured relationship modeling.
3Device complexity
If conventional processing methods are used, then system simplicity is maintained, but productivity decreases
Solution Approach 1:
The patent implements a universal graph database system that handles multiple data modalities and processing tasks through a single unified platform. The graph engine provides multi-functional capabilities including data ingestion, storage, querying, semantic traversal, and hypothesis generation within one system architecture. This universal approach maintains relative system simplicity by avoiding multiple specialized tools while dramatically improving productivity through integrated parallel processing and efficient graph algorithms that can simultaneously analyze diverse biomedical data types.
Data Source
AI summary
Systems and methods disclosed herein may include a parallel-processing graph-database solution for protein-sequence analytics to determine a viable therapeutic for a given condition, and may include: determining a protein sequence for the given condition; using sequence database to compare a query sequence of the sequence of the given condition with sequences of other known proteins in the sequence database using the sequence database to determine a similarity of the query sequence with sequences of the other known proteins in the sequence database based on the comparison; and querying a graph database based on the similarity of sequences to identify potential therapeutics that could be have an inhibitory effect on the given condition.