AN EMERGING MARINE BIOTECHNOLOGY: MARINE DRUG DISCOVERY
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Marine natural resources offer an opportunity to discover a novel chemical diversity withinterest ing pharmacologically active compounds to treat many diseases such as cancer,inflammation, bacterial and parasitic infections, and many other diseases. Marine drug discoveryis a rising area in marine biotechnology. Several hits of marine-derived drug compounds wereapproved; two of them are Ziconotide and Trabectedin. In 2004, Ziconotide was approved as paintreatment drugs in the United States and Europe. Then, in 2007, Trabectedin was also approvedas anticancer drug in Europe. The main problem in marine drug discovery research is materialsupply problem. Up till now, strategies to overcome the problem are “Pharmaceutical aquaculture”of biologically active marine biota and chemical synthesis approach. Chemical synthesis approachis feasible solution to be used, especially when working with less complex structure of compounds.However, when working with structurally complex compounds where total or even semi synthesiswas very difficult to be provided, aquaculture can be a solution. Currently, the use of microbiology,biochemistry, genetic, bioinformatics, genomic and meta-genomic has been intensifying in orderto have a better result in marine natural product drug discovery. As chemical synthesis needs anexpensive investment of advanced technology and highly skilled human resources, thuspharmaceutical aquaculture is more practicable to overcome the material supply insufficiency inIndonesia. Up till now, many Indonesian marine bioprospectors have been working with culturablemarine microorganism to produce bioactive compounds and some others starting to work withgenomic and metagenomic-based drug discovery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it