Dayhoff is an autonomous multi-agent bioinformatics framework developed for computational biology and public health applications. Named in honor of Dr. Margaret Belle Dayhoff, a pioneer in bioinformatics, this platform was released in May 2026 by the Humanitarians AI research team and introduces large language model-based agent orchestration technology to the life sciences. It adopts an architecture that distributes heterogeneous data analysis tasks under a central coordination layer into six dedicated agent layers, including genomic analysis, epidemiological tracking, and molecular modeling, ranging from biological sequence analysis to wastewater-based epidemiology monitoring. Just as GPT understands large-scale text contexts and generates the next sentence, Dayhoff performs intelligent distributed computations by integrating complex biological multimodal signals, such as genomic sequences and Raman spectroscopy data, to design custom protein structures and predict the spread of novel pathogens. In conventional computational biology research environments, individual specialized software, such as protein structure prediction (AlphaFold), molecular dynamics simulations (GROMACS), and wastewater-based epidemiology analysis, are fragmented, creating a significant bottleneck due to the need for researchers to manually transform and validate intermediate data. Dayhoff overcomes this limitation by introducing a Cross-Agent Validation architecture and a dynamic resource allocation mechanism, providing an automated collective intelligence structure that automatically detects and corrects conflicts in analysis results between agents. Unlike a single deep learning model limited to a specific task, it orchestrates sub-modules such as PredictaBio, a protein design sub-module, and RAMAN Effect, a Raman spectroscopy analysis module, to manage everything from gene design to public health monitoring in a single integrated pipeline. Biotechnology and public health researchers can leverage the Dayhoff framework to rapidly build pipelines for responding to emerging infectious diseases and early warning systems for infectious diseases. For example, when surface-enhanced Raman spectroscopy (SERS) measurement data collected from wastewater treatment plants is input, the Raman analysis agent decodes the complex spectral signals to detect the presence of variant pathogens, and the molecular modeling agent and genomic agent are immediately orchestrated to automatically design candidate antibody gene sequences tailored to the surface protein binding sites of the pathogen. This series of interconnected processes, from data collection to the derivation of protein candidates, is executed through distributed agent collaboration, significantly reducing the initial biomarker identification and vaccine candidate design period, which previously took several weeks.

Dayhoff is an autonomous Multi-Agent bioinformatics framework developed for computational biology and public health applications. Continuing the academic legacy of Dr. Margaret Belle Dayhoff, a pioneer in bioinformatics, this platform was released in May 2026 by the Humanitarians AI research team and implements large language model-based agent orchestration technology in the field of life sciences. From biological sequence analysis to wastewater epidemiology monitoring, it centralizes heterogeneous data analysis tasks under a coordinating layer, encompassing genomic analysis, epidemiological tracking, and molecular modeling.

Surface-Enhanced Raman Spectroscopy (SERS) for Wastewater Epidemiology Monitoring: Real-time decoding of spectral data from urban wastewater treatment plants using RAMAN Effect agents to enable early detection of pathogen variants and track their spread.