Identification of Small-Molecule Inhibitors of Human Inositol Hexakisphosphate Kinases by High-Throughput Screening
Bibliographic record
Abstract
Inositol hexakisphosphate kinases (IP6Ks) catalyze pyrophosphorylation of inositol hexakisphosphate (IP6) into inositol 5-diphospho-1,2,3,4,6-pentakisphosphate (IP7), which is involved in numerous areas of cell physiology including glucose homeostasis, blood coagulation, and neurological development. Inhibition of IP6Ks may be effective for the treatment of Type II diabetes, obesity, metabolic complications, thrombosis, and psychiatric disorders. We performed a high-throughput screen (HTS) of 158 410 compounds for IP6K1 inhibitors using a previously developed ADP-Glo Max assay. Of these, 1206 compounds were found to inhibit IP6K1 kinase activity by more than 25%, representing a 0.8% hit rate. Structural clustering analysis of HTS-active compounds, which were confirmed in the dose-response testing using the same kinase assay, revealed diverse clusters that were feasible for future structure-activity relationship (SAR) optimization to potent IP6K inhibitors. Medicinal chemistry SAR efforts in three chemical series identified potent IP6K1 inhibitors which were further validated in an orthogonal LC-MS IP7 analysis. The effects of IP6K1 inhibitors on cellular IP7 levels were further confirmed and were found to correlate with cellular IP6K1 binding measured by a high-throughput cellular thermal shift assay (CETSA).
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".