Bibliographic record
Abstract
been estimated that ~40% of the attrition rate of candidate-drugs has been associated with poor pharmacokinetic features and toxicity.Poor solubility is, particularly, a very significant impediment in the drug development efforts.The solubility of a drug molecule is vital for its' bioavailability.If an orally administered drug is not sufficiently soluble, then it could not be fully absorbed into the blood circulation and will be expelled from the gastrointestinal tract before reaching its' site of action.Nevertheless, hydrophobicity and innate low-water solubility are gradually becoming rather unsurprising characteristics of early hits, lead compounds and even in some market-approved drugs.3 Nearly 60%-90% of the compounds that are currently being developed exhibit poor water-soluble features 3,4 and categorized under the Biopharmaceutical Classification System (BCS) classes II (low solubility and high permeability) and IV (low solubility and low permeability).[4][5][6] This is mainly because, most of the ligand-binding sites in the target proteins are secluded from the aqueous environment and hence hydrophobic compounds are generally preferred (or developed) in order to gain high binding affinity and activity against the target(s).3,7 In addition, recently, the drug discovery research is also seeing a paradigm shift from enzymes to more complicated therapeutic targets, such as ion channels, protein-protein interfaces, kinases and nuclear receptors.1,5,[8][9][10] Such challenging targets generally demand more lipophilic drugs, which also involves high-crystal energies from strong intermolecular interactions.5 These factors altogether tend to adversely impact the solubility of compounds.
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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.061 | 0.026 |
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".