In Silico Prediction of Steroids and Triterpenoids as Potential Regulators of Lipid Metabolism
Bibliographic record
Abstract
This review focuses on a rare group of steroids and triterpenoids that share common properties as regulators of lipid metabolism. This group of compounds is divided by the type of chemical structure, and they represent: aromatic steroids, steroid phosphate esters, highly oxygenated steroids such as steroid endoperoxides and hydroperoxides, α,β-epoxy steroids, and secosteroids. In addition, subgroups of carbon-bridged steroids, neo steroids, miscellaneous steroids, as well as synthetic steroids containing heteroatoms S (epithio steroids), Se (selena steroids), Te (tellura steroids), and At (astatosteroids) were presented. Natural steroids and triterpenoids have been found and identified from various sources such as marine sponges, soft corals, starfish, and other marine invertebrates. In addition, this group of rare lipids is found in fungi, fungal endophytes, and plants. The pharmacological profile of the presented steroids and triterpenoids was determined using the well-known computer program PASS, which is currently available online for all interested scientists and pharmacologists and is currently used by research teams from more than 130 countries of the world. Our attention has been focused on the biological activities of steroids and triterpenoids associated with the regulation of cholesterol metabolism and related processes such as anti-hyperlipoproteinemic activity, as well as the treatment of atherosclerosis, lipoprotein disorders, or inhibitors of cholesterol synthesis. In addition, individual steroids and triterpenoids were identified that demonstrated rare or unique biological activities such as treating neurodegenerative diseases, Alzheimer’s, and Parkinson’s diseases with a high degree of certainty over 95 percent. For individual steroids or triterpenoids or a group of compounds, 3D drawings of their predicted biological activities are presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".