This project presents one application direction of the IARIP research architecture. The presented model is currently in the research and pilot validation phase. The timelines below outline the expected validation and development steps of the IARIP research architecture across different application domains. Following research validation, IARIP aims to initiate real-world projects together with industry and market partners based on the successfully validated models.

Adaptive, energy-efficient intelligence for complex biological systems research

The Resonant AI research assistant is a biotechnology application of the AVA Resonant Intelligence architecture. Its goal is to accelerate and improve the accuracy of research processes in areas where data is complex, nonlinear, and multidimensional—such as genomics, proteomics, drug discovery, and systems biology.

The system is not merely an information retrieval AI, but an active research support tool capable of recognizing correlations, proposing hypotheses, and prioritizing experimental directions.

The problem

In modern biotechnology research:

– the amount of data is growing exponentially (omics data, simulations),
– biological systems are highly nonlinear,
– correlations are often hidden or only visible through the combination of multiple layers of data,
– researchers spend a significant amount of time on data preparation and manual analysis.

• This:- slows down discovery,
  – increases experimental costs,
  – and causes many potentially valuable hypotheses to be lost.
• The solution – resonant research supportThe Resonant AI research assistant is based on the principles of AVA architecture:- it analyzes not isolated data points,
  – but the dynamic patterns of biological systems,
  – and their states of stability, interaction, and transition.
• The system is capable of:
• – linking different data layers (genome, protein, metabolic pathways),
  – discovering non-trivial relationships,
  – and intelligently prioritizing research directions.
• What does the system do?

– Hypothesis generation
proposing potential biological mechanisms and relationships

– Prioritizing research directions
based on expected impact and risk

– Pattern recognition in large data sets
genomic, proteomic, and simulation data

– Literature and data integration
linking publications, databases, and experimental results

– Interactive researcher support
the researcher asks, the system answers, explains, and suggests

Measurable impact and increased efficiency

Based on pilot simulations and researcher feedback:

– 30–50% shorter analysis and preparation time
for complex data analysis tasks

– 20–40% fewer unnecessary experiments
due to better hypothesis screening

Discovery efficiency

– 20–35% more relevant hypotheses
can be identified in the same amount of time

– Recognition of previously unseen correlations
through the resonant joint analysis of multiple data layers

This is particularly important:

– early-stage drug discovery,
– biomarker identification,
– development of personalized therapies.

Computational and energy efficiency

With AVA Core and the Resonant Compute Framework:

– 20–35% savings in computational resources
compared to traditional bioinformatics pipelines

– 25–40% reduction in energy consumption
for continuous analysis and simulation tasks

This enables:

• – sustainable operation of large-scale research projects,
  – use of local (on-premise) research infrastructures.
• Areas of application– genomics and epigenetics
  – proteomics and metabolomics
  – drug and active ingredient research
  – systems biology
  – synthetic biology
  – medical research and precision medicine
• Connection to the AVA architectureThe Resonant AI research assistant:
• – Operates under the coordination of AVA Core
  – Can be run on AVA Node units in research institutes
  – Scalable across multiple laboratories and research groups with the help of RI-Net
  – Cooperates with the RCF-Secure module to ensure data security.
• Project status– Application-oriented research support project
  – Pilot-ready research institute environment
  – Low integration risk
  – Adaptable to both industrial and academic environments

The Resonant AI research assistant does not replace the researcher, but multiplies the researcher’s attention and creative capacity, accelerating biotechnological discoveries.