Development of Specific “Drug‐Like Property” Rules for Carboxylate‐Containing Oral Drug Candidates
Bibliographic record
Abstract
The carboxylate moiety is an important pharmacophore in the medicinal chemist's arsenal and is sometimes an irreplaceable functionality in drug-target interactions. Thus, practical guidance on its use in the most optimized manner would be a welcome addition to rational drug design. Key physicochemical and ADMET-PK properties from a dataset of drugs containing a carboxylate (COOH) moiety were assembled and compared with those of a broader, general drug dataset. Our main objective was to identify features specific to COOH-containing oral drugs that could be converted into simple rules delineating the boundaries within which prospective COOH-containing chemical series and COOH-containing drug candidates would be reasonably expected to possess properties suitable for oral administration. These specific "drug-like" property rules include molecular weight, the number of rotatable bonds, the number of hydrogen bond donors and acceptors, predictions of lipophilic character (calculated log P and log D values), topological polar surface area (TPSA), and the pK(a) value of the carboxylate moiety. Similar to the various sets of criteria that have emerged over the past decade and which have significantly reshaped the way medicinal chemists think about preferred drug chemical space, we propose these specific COOH "drug-like" property rules as a guide for the design of superior COOH-containing drug candidates and as a tool to better manage the liabilities generally associated with the presence of a COOH moiety.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".