Knowledge Graph for Contextualizing Molecules in Drug Discovery
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Solution Overview
Problem
Current scientific efforts in medical research are dispersed and inefficient in contextualizing molecules within the vast and complex field of biological knowledge, making it difficult to amalgamate information for pharmacokinetics and other biology-related fields.
Innovation Solution
A system and method utilizing a knowledge graph populated with all known molecules, which analyzes input molecules using various similarity indexes to generate a contextualized summary of related target molecules, including proteins, diseases, and biological mechanisms, incorporating scientific literature, governmental data, and other biological data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If scientists manually analyze and integrate biological knowledge across multiple sources, then comprehensive understanding of molecules can be achieved, but the process becomes extraordinarily difficult and inefficient due to compounding complexity
Solution Approach 1:
The patent introduces an artificial intelligence system as an intermediary between the vast biological knowledge sources and researchers. This AI intermediary automatically integrates data from multiple sources including scientific literature, databases, and experimental results, transforming the extraordinarily difficult manual integration process into an automated computational task that handles complexity without proportionally increasing difficulty
Solution Approach 2:
The patent replaces the mechanical manual process of knowledge integration with an artificial intelligence-based automated system. Instead of researchers manually searching, filtering, and synthesizing information from multiple sources, the AI system performs these operations computationally, substituting human cognitive effort with automated information processing capabilities
2Adaptability or versatility
If dispersed scientific efforts are maintained across multiple independent sources, then diverse perspectives are preserved, but the efforts fail to amalgamate into a unified body of knowledge
Solution Approach 1:
The patent merges dispersed scientific efforts by integrating data from multiple independent sources including scientific literature, databases, and experimental results into a unified knowledge structure. The AI system combines these diverse inputs while preserving their individual contributions, creating a comprehensive amalgamated body of biological knowledge that maintains the versatility of source diversity while achieving unified integration
3Measurement precision
If researchers attempt to analyze all available biological data, then complete contextual understanding can be achieved, but the time and computational resources required become prohibitively large
Solution Approach 1:
The patent applies preliminary action by pre-processing and organizing biological data into structured formats before actual analysis occurs. The AI system pre-integrates data from multiple sources, pre-identifies relevant information, and pre-organizes knowledge structures, so that when researchers need contextual understanding, the preparatory work has already been completed, significantly reducing the time required for comprehensive analysis
Data Source
AI summary
A system and method that given one or more input molecules, produces a contextualized summary of characteristics of related target molecules, e.g., proteins. Using a knowledge graph which is populated with all known molecules, input molecules are analyzed according to various similarity indexes which relate the input molecules to target proteins or other biological entities. The knowledge graph may also comprise scientific literature, governmental data (FDA clinical phase data), private research endeavors (general assays, etc.), and other related biological data. The summary produced may comprise target proteins that satisfy certain biological properties, general assay results (ADMET characteristics), related diseases, off-target molecule interactions (non-targeted molecules involved in a specific pathway or cascade), market opportunities, patents, experiments, and new hypothesis.