Characterization of Drug-like Chemical Space for Cytotoxic Marine Metabolites Using Multivariate Methods
Bibliographic record
Abstract
Abstract In the last few decades, marine metabolites have been exploited to find commercially viable products in several areas. In this article, molecular descriptors [log P, mass, total polar surface area (TPSA), H-bond donor, H-bond acceptor, and the number of rotatable bonds] for?the marine-derived cytotoxic metabolites were calculated and compared with marketed anticancer drugs to understand their position in the drug-like space. Marine-based cytotoxic metabolites are divided into highly toxic (HT) and moderately toxic (MT) classes. The marketed anticancer drugs complied well with Lipinski?s rule of five for all molecular descriptors. The majority of HT and MT metabolites complied solely with H-bond donors and a number of rotatable bonds with the Lipinski cutoff values.?Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were also performed using 73 molecular descriptors on an ensemble of highly cytotoxic or moderately cytotoxic marine metabolites and the marketed reference drugs. The HCA results showed that 12% of marine metabolites clustered with the marketed anticancer drugs and many of them had structural scaffold homology. The PCA results revealed the presence of a clear distinction between the cytotoxic marine metabolites and the marketed anticancer drugs. Results indicate that mass, TPSA, and log P are the vital parameters and the careful optimization of these parameters for marine cytotoxic?metabolites may generate more meaningful anticancer candidates in the future.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".