The metal(loid)s’ dilemma. What's the next step for a new era of inorganic molecules in medicine?
Bibliographic record
Abstract
In this paper, we critically examine the key challenges associated with the development of inorganic drugs, a field that remains underrepresented despite its significant therapeutic potential. Currently, most clinically approved pharmaceuticals are organic compounds, a trend driven by multiple interconnected factors that have historically limited the adoption and regulatory approval of metal(loid)-based entities. These challenges include issues related to stability, selectivity, pharmacokinetics, and potential toxicity, which require systematic investigation and innovative solutions. Nevertheless, the profound clinical impact of approved inorganic drugs-particularly transition metal(loid)-based agents for both therapeutic and diagnostic applications-is well-established. The success of these compounds underscores the need for expanded research efforts and optimized clinical protocols to fully harness the advantages of metal-based pharmaceuticals. In this context, we explore emerging strategies to overcome current limitations and accelerate the development of next-generation inorganic drugs. These include the rational design of metal-based therapeutics, the integration of advanced metallomics and metalloproteomics, and the application of AI-driven predictive modeling to improve drug selectivity, bioavailability, and safety. By overcoming these challenges through an interdisciplinary approach, metal-based medicine will advance significantly, expanding its impact across a wide range of therapeutic applications.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".