AI Systems for In Silico Discovery of Immune Modulators
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Solution Overview
Problem
The rapid accumulation of biological knowledge has made it increasingly difficult for humans to process and develop meaningful experimental hypotheses, particularly in the area of identifying agents that modulate immunological cell activity, such as T regulatory cells, where existing methods lack effective utilization of fuzzy logic and artificial intelligence for drug-target interactions.
Innovation Solution
The use of artificial intelligence systems and fuzzy logic to identify compounds capable of modulating T regulatory cell activity by assessing their ability to interact with FoxP3 protein, performing chemical optimization, and evaluating their activity through in vitro assays, while also considering various macrophage and T regulatory cell functionalities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional human methods are used to process biological knowledge and develop experimental hypotheses, then human intuition and creativity are utilized, but the ability to process and develop meaningful experimental hypotheses becomes increasingly difficult as biological knowledge accumulates at an accelerating pace
Solution Approach 1:
The patent replaces human cognitive processing with an AI-based system that uses fuzzy logic and machine learning algorithms to analyze biological data and generate experimental hypotheses. The system processes large volumes of biological knowledge automatically, substituting human manual analysis with computational mechanisms that can handle the accelerating accumulation of biological data without cognitive overload
Solution Approach 2:
The patent introduces fuzzy logic as an intermediary between raw biological data and experimental hypotheses. Fuzzy logic serves as a mediator that processes uncertain and imprecise biological information through defined rules and algorithms, transforming complex biological knowledge into actionable experimental hypotheses in a systematic manner
2Productivity
If fuzzy logic and AI-based data analysis are used to identify drug-target interactions, then the identification process becomes more efficient and accurate, but this approach has not been previously applied to immunological cell activity modulation
Solution Approach 1:
The patent creates a universal AI-based platform that can identify drug-target interactions across different biological systems. The fuzzy logic framework and machine learning models are designed to be adaptable, allowing the same system to handle both general drug-target interactions and specific immunological cell activity modulation, thereby achieving multi-functionality without requiring separate specialized systems
Solution Approach 2:
The patent applies parameter changes by adjusting the fuzzy logic rules and AI model parameters to specialize the general identification system for immunological applications. By modifying the input parameters, evaluation criteria, and output interpretations, the system adapts to the specific requirements of identifying agents that modulate immunological cell activity while maintaining the core efficiency of the AI-based approach
Data Source
AI summary
Disclosed are methods, means and systems of identifying compounds and augment T regulatory cell activity and/or number in vitro and/or in vivo utilizing deep learning approaches. Systems for screening compound libraries in silico are provided as well as laboratory methods of testing modulation of T regulatory cell activity and/or numbers. Results provided by the disclosure will serve as the basis for increasing T regulatory cells, which is desirable in situations of autoimmunity or organ transplantation. In situations of oncology or infectious disease reduction of T regulatory cell number and/or activity is desired.