Development of a Data-Driven Psychedelic Therapy Network

The development of a data-driven network aims to provide objective, evidence-based guidance for determining the most appropriate psychedelic therapies for distinct psychiatric conditions. By integrating research literature, clinical trial data, and anonymous user reports, this approach attempts to map the psychedelic experience across multiple substances. The methodology focuses on identifying biotypes associated with specific patient outcomes, thereby reducing potential biases in treatment decision-making. The project specifically assesses the therapeutic potential of substances like MDMA and ayahuasca, particularly for conditions such as PTSD and depression. Network topology and machine learning tools help visualize how different substances relate to various subjective experiences and clinical metrics. This framework seeks to establish a foundation for precision medicine in psychedelic therapy, moving away from one-size-fits-all models and toward tailored treatments based on empirical data.