AI Viral RNA Mutation Prediction via Generative Modeling
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Solution Overview
Problem
Current methods for identifying and predicting viral mutations in RNA strains are slow, costly, and inefficient, particularly in simulating the large number of possible variations in the viral genome, which delays the identification of emerging infections and vaccine development.
Innovation Solution
A method and system using AI models to determine similarity between new viral RNA strains and reference strains, calculating a strain score, identifying mutation sites, and predicting mutations through generative modeling, enabling early identification and preparation for potential pathogens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If in-vitro solutions are used for viral strains identification and prediction, then measurement precision is improved, but productivity deteriorates due to slow and cost-intensive physical cultures
Solution Approach 1:
The patent creates in-silico copies of viral strains by generating synthetic genomic sequences that replicate the biological properties of actual viral strains. These digital twins allow researchers to simulate viral behavior, mutations, and epidemiological patterns without requiring physical viral cultures, thereby maintaining measurement precision while dramatically improving productivity through computational speed.
Solution Approach 2:
The patent replaces the mechanical biological system of physical viral cultures with a computational simulation system. Instead of growing and manipulating actual viral particles in laboratories, the system uses AI models and computational algorithms to simulate viral replication, mutation, and transmission dynamics, eliminating the time-consuming and resource-intensive nature of in-vitro experiments.
2Productivity
If current in-silico experiments are used for viral genome variation simulation, then productivity is improved, but measurement precision deteriorates due to inability to simulate large number of mutations in reasonable time
Solution Approach 1:
The patent performs preliminary action by pre-training AI models on extensive databases of known viral sequences, mutations, and epidemiological outcomes before actual prediction tasks. The system pre-generates and stores synthetic viral strain data representing various mutation scenarios, allowing it to rapidly predict mutation outcomes without performing time-consuming simulations during the actual prediction phase.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting simulation parameters such as mutation rates, selection pressures, and epidemiological conditions based on the specific viral strain and context being analyzed. The AI model learns optimal parameter ranges from training data and adapts them to different scenarios, improving both the speed and accuracy of mutation predictions across diverse viral contexts.
Data Source
AI summary
Disclosed herein is method and a system for predicting mutations in Ribonucleic acid (RNA) strains. In an embodiment, a similarity between a new viral RNA strain and reference RNA strains is determined. Further, a strain score for the new viral RNA strain is calculated based on the similarity between the new viral RNA strain and the reference RNA strains. Subsequently, mutation sites for the new viral RNA strain are identified by generating spatial nearness data corresponding to the reference RNA strains based on comparison between the strain score of the new viral RNA strain and the reference RNA strains. Finally, mutations of the new viral RNA strain are predicted by performing a generative modelling of a sequence of the new viral RNA strain with reference to the mutation sites of the new viral RNA strain.