The AI in Aion

The driving force behind AION is the use of breakthrough Artificial Intelligence technologies. AI is used to break down the genetic make-up of plants and mushrooms to determine the most effective compounds for drug development and have that directly applied to specific diseases and patient profiles. 

Aion incorporates the highest level AI for the analysis of the plant's make-up, we utilize AI extensively in our drug development process and optimization of compositions for use in patients suffering from multiple diseases including multiple cancers, mental health disorders, inflammation, obesity and viruses.

Vast arrays of data on the therapeutic effects of a plurality of medical mushrooms, mushroom extracts, cannabinoids, flavonoids, terpenes, and optional ingredients (for example, omega-3, B and D vitamins, and a host of others) are analyzed to generate profiles of optimized combinatorial compositions for therapeutic use in specific diseases. Aion uses multiple AI algorithm techniques including, but not limited to classification (naive Bayes, decision tree, random forest, Support Vector Machines, K Nearest Neighbor), regression (linear, lasso, logistic, multivariate), clustering (K-means, fuzzy C-means, expectation-maximization, hierarchical), and multiple combinatorial algorithms. 

Aion is able to utilize data from original research conducted by Aion scientists at our research facilities, as well as reviews of the scientific literature through the incorporation of NLP (natural language processing).

The AI and methods employed by Aion are specifically designed to optimize the compositions with respect to each particular disease and it's specific variants (for example colon cancer versus lung cancer) and is also able to be optimized for individual patients based on their specific health and disease profile, thus creating "Personalized Medicines" that are optimized for each individual. 

The "personalized medicines" use AI to take into account patient-specific data (for example blood profiles, enzymes, bioinformatic data, symptom profiles, cardiac health, health history, etc) and then further optimize and personalize the drug composition based on the patient-specific and disorder-specific data. Formulations can be further optimized during the treatment and altered based on the results of initial treatment, subsequent treatments, and subsequent lab tests of the individual patient.