Indole Alkaloids from Psychoactive Mushrooms: Chemical and Pharmacological Potential as Psychotherapeutic Agents
Bibliographic record
Abstract
Neuropsychiatric diseases such as depression, anxiety, and post-traumatic stress represent a substantial long-term challenge for the global health systems because of their rising prevalence, uncertain neuropathology, and lack of effective pharmacological treatments. The approved existing studies constitute a piece of strong evidence whereby psychiatric drugs have shown to have unpleasant side effects and reduction of sustained tolerability, impacting patients' quality of life. Thus, the implementation of innovative strategies and alternative sources of bioactive molecules for the search for neuropsychiatric agents are required to guarantee the success of more effective drug candidates. Psychotherapeutic use of indole alkaloids derived from magic mushrooms has shown great interest and potential as an alternative to the synthetic drugs currently used on the market. The focus on indole alkaloids is linked to their rich history, their use as pharmaceuticals, and their broad range of biological properties, collectively underscoring the indole heterocycle as significant in drug discovery. In this review, we aim to report the physicochemical and pharmacological characteristics of indole alkaloids, particularly those derived from magic mushrooms, highlighting the promising application of such active ingredients as safe and effective therapeutic agents for the treatment of neuropsychiatric disorders.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".