AI-driven phytogenic additives are transforming aquaculture by accelerating discovery and enhancing understanding of natural compounds. Advanced technologies now enable rapid screening and detailed biological analysis, while AI predicts bioactivity and designs synergistic blends. This shift promises to overcome traditional research bottlenecks, but what future innovations will further reshape the industry remains to be seen.
Chief Executive Officer
ISR-BIO
Chief Technology Officer
ISR-BIO
Today, feed additives are a vital component of modern aquaculture nutrition, aimed at enhancing growth performance, improving feed efficiency, and bolstering disease resistance. As global seafood demand continues to rise, sustainable practices have become essential. Phytochemicals derived from plant extracts, in particular, have emerged as powerful natural additives. Rich in bioactive compounds, these phytogenics exhibit antioxidant, anti-inflammatory, and antimicrobial properties, helping to reduce oxidative stress and strengthen fish immune systems as a natural alternative to synthetic growth promoters and antibiotics.
The primary challenge has been the slow, complex, and costly process of discovering and validating these compounds. With tens of thousands of natural compounds to screen against hundreds of pathogens and cellular mechanisms, the traditional research approach is economically unfeasible. A full research process for a single additive can cost upwards of $500,000, with a success rate of less than 10%. Fortunately, technological innovation is dramatically changing this landscape, accelerating the path from discovery to application.
OVERCOMING THE RESEARCH BOTTLENECK
The main obstacle in harnessing the full potential of phytogenics has been the sheer scale of the natural world. Identifying the most potent compounds has traditionally been a monumental task. Today, a suite of advanced technologies allows researchers to screen, analyze, and validate these compounds with unprecedented speed and precision.
The first leap forward has been High-Throughput Screening (HTS), which uses automated platforms to rapidly test thousands of plant extracts against specific pathogens or on fish cell lines. This technology quickly narrows the vast library of natural compounds to a manageable number of promising candidates for in-depth study.
While HTS tells us if a compound works, a suite of “omics” technologies (genomics, transcriptomics, proteomics, and metabolomics) tells us how it works. This multi-faceted approach provides a holistic view of a compound’s biological impact at the molecular level. Together, these technologies create a detailed map of a compound’s journey through the fish’s system, confirming its efficacy and ensuring its safety.
THE COMING RISE OF AI AND MACHINE LEARNING
The most transformative advance will be the full integration of Artificial Intelligence (AI) and machine learning into the research pipeline. AI algorithms will analyze the chemical structures of thousands of phytochemicals and predict their bioactivity before they ever enter a lab. By training on vast datasets from past HTS and ‘omics’ studies, these models will identify novel candidates with a high probability of success, saving immense resources.
Furthermore, AI will be instrumental in making sense of the complex data generated by ‘omics’ research. Machine learning will pinpoint subtle patterns in gene expression or metabolic changes that would be invisible to the human eye, linking specific compounds to enhanced immune function or improved gut health. This predictive power will revolutionize the discovery pipeline, moving it from a process of trial-and-error to one of intelligent design.
HOW AI WILL REVOLUTIONIZE ADDITIVE DISCOVERY
The scaling of phytogenic research, supercharged by Artificial Intelligence, will trigger a profound and lasting transformation in global aquaculture. AI will fundamentally change not just the speed, but the very nature of how new additives are discovered, developed, and deployed. This shift will move the industry from a reactive to a predictive model of animal health and sustainability, built on four key pillars of innovation.
1. Predictive Discovery: From Haystack to Pinpoint
The traditional method of finding new additives relies on physically screening thousands of compounds, a costly and inefficient process. AI will completely invert this model. By leveraging machine learning algorithms trained on massive chemical and biological datasets, researchers will perform in silico (computational) screening of millions of molecules. These models will predict a compound’s potential efficacy, safety, and even its mechanism of action based on its chemical structure alone. This will change the form of discovery from a brute-force physical search to a highly targeted computational one. The impact will be a dramatic acceleration of the R&D pipeline; AI will identify a shortlist of high-potential candidates in weeks, a task that once took years, allowing research efforts and resources to be focused only on the most promising options.
2. Deciphering Biological Mechanisms at Scale
Understanding how an additive works will be crucial for optimizing its use. AI’s ability to integrate and analyze enormous, multi-layered ‘omics’ datasets will provide a panoramic view of an additive’s biological impact. Instead of studying a single pathway, AI will map the entire systemic response of a fish to a phytogenic compound. This will change the form of research from isolated analysis to holistic systems biology. The impact will be the ability to design additives with surgical precision. For example, AI will identify a compound that not only inhibits a pathogen but also specifically upregulates the genes for mucus production in the gut lining, creating a dual-action solution. This deep mechanistic insight will lead to more reliable, potent, and predictable products.
3. Engineering Intelligent Synergistic Solutions
The most effective natural solutions will be complex mixtures, not single ingredients. While human formulators are limited in the number of combinations they can test, AI will explore a virtually infinite design space. Machine learning algorithms will analyze millions of potential interactions between different phytogenics, probiotics, prebiotics, and enzymes to design “intelligent blends” with synergistic effects. This will shift the form of product development from simple mixing to computational engineering of complex formulas. The impact will be the creation of multi-targeted additives that offer holistic benefits—for instance, a single product that is simultaneously antimicrobial, anti-inflammatory, and a gut microbiome modulator. These AI-designed solutions will be far more powerful and cost-effective than their individual components.
4. Digital Twins and Virtual Trials
One of the biggest bottlenecks in additive development is the reliance on lengthy and expensive animal trials. AI will offer a revolutionary alternative: The creation of “digital twins”. These will be sophisticated virtual models of a fish or an entire aquaculture system, built from biological data. Researchers will use these digital twins to conduct thousands of virtual trials, simulating how a newly designed additive will perform under different conditions (e.g., varying water temperatures, diets, or disease pressures) before ever entering a live animal study. This will change the form of product validation, allowing for extensive in silico testing to refine dosage, timing, and formulation. The impact will be a massive reduction in the cost, time, and number of animals required for R&D, leading to a much faster path from concept to commercial product.
CONCLUSION
The journey from a plant in a field to a powerful, health-promoting feed additive will be fundamentally transformed by technology, with Artificial Intelligence leading the charge. The once-overwhelming challenge of navigating nature’s vast chemical library will become a task of precision and prediction. AI-driven discovery, combined with high-throughput screening and ‘omics’ technologies, will provide an unprecedented understanding of how phytogenics function at a molecular level. This will enable the development of targeted, synergistic, and predictive natural solutions. As a result, phytogenics will evolve from a niche alternative into a cornerstone of modern aquaculture nutrition, heralding a new, AI-powered era of sustainability, efficiency, and profitability for the industry worldwide.
By leveraging recent advances in artificial intelligence, ISR-BIO has developed the industry’s first dedicated AI platform for predictive screening in the field of phytogenic feed additives. This platform is designed not only to facilitate the discovery of novel bioactive compounds but also to identify new applications for existing additives across different aquaculture species—revealing functional potentials that may have previously gone unrecognized. Through the integration of this AI-driven approach, research teams can significantly reduce development timelines, expand discovery throughput, and allocate resources more efficiently by minimizing investment in low-probability candidates. This represents a shift from exploratory, trial-based research models toward a more strategic, data-informed R&D framework.
About Francisco Arancibia
A Civil Industrial Engineer from the University of Chile, Francisco Arancibia is a seasoned entrepreneur with a passion for disruptive innovation. Throughout his career, he has gained extensive experience in R&D, product development, and strategic consulting across various industries, including in ventures he founded himself. In November 2023, Arancibia launched ISR-BIO, where he is leading the strategy to pioneer an AI-driven revolution in animal health.About Roberto Ibanez
A dual-certified Mechanical Civil Engineer and Computer Science Engineer from the University of Chile, Roberto Ibanez has 10-years of experience at the intersection of artificial intelligence and biotechnology. He has held key positions as the Director of Artificial Intelligence at Protera and as an AI Scientist at Kura Biotech. Ibanez is leading the development of CORAL AI, the world’s first artificial intelligence platform specifically focused on animal health.
