Large Language Model‐Assisted Genotoxic Metal‐Phenolic Nanoplatform for Osteosarcoma Therapy
Bibliographic record
Abstract
Abstract Osteosarcoma, a leading primary bone malignancy in children and adolescents, is associated with a poor prognosis and a low global fertility rate. A large language model‐assisted phenolic network (LLMPN) platform is demonstrated that integrates the large language model (LLM) GPT‐4 into the design of multifunctional metal‐phenolic network materials. Fine‐tuned GPT‐4 identified gossypol as a phenolic compound with superior efficacy against osteosarcoma after evaluating across a library of 60 polyphenols based on the correlation between experimental anti‐osteosarcoma activity and multiplexed chemical properties of polyphenols. Subsequently, gossypol is then self‐assembled into Cu 2+ ‐gossypol nanocomplexes with a hyaluronic acid surface modification (CuGOS NPs). CuGOS NPs has demonstrated the ability to induce genetic alterations and cell death in osteosarcoma cells, offering significant therapeutic benefits for primary osteosarcoma tumors and reducing metastasis without adverse effects on major organs or the genital system. This work presents an LLM‐driven approach for engineering metal‐organic nanoplatform and broadening applications by harnessing the capabilities of LLMs, thereby improving the feasibility and efficiency of research activities.
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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) | 0.000 | 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".