Synthesis, DFT Calculations, and Molecular Docking Study of New Aggregation‐Induced Emission Lumiogens Based on Thieno[2,3‐d]pyrimidine Moiety
Bibliographic record
Abstract
Abstract The discovery of novel Aggregation‐Induced Emission (AIE) systems based on heterocyclic compounds holds significant potential. In this study, a series of new AIE systems based on thieno[2,3‐d]pyrimidine moiety synthesized and characterized by spectroscopic analyses. These compounds exhibited weak emission in DMSO solution but displayed strong solid‐state fluorescence at λmax=556, 527, 527, and 515 nm for compounds 7 a, 7 b, 7 c, and 7 e respectively. Additionally, compound 10 exhibited emission at 480 nm in DMSO, which was red‐shifted to 490 nm in the solid state. Furthermore, the AIE behavior for these compounds was investigated in different DMSO/H2O fractions. Compounds 7 a–c, 7 e, and 10 exhibits a typical AIE behavior since these compounds showed weak fluorescence intensity in pure DMSO but sharply increased while the water content reached 80 % in the case of compounds 7 a–c, and 7 e, and 90 % in compound 10. Moreover, Density Functional Theory (DFT) calculations supported the role of molecular packing and intermolecular interactions in modulating the luminescence properties. Molecular docking studies suggested the potential of these AIE compounds as anticancer agents. Compound 7 a exhibits a strong binding affinity of −9.6 kcal/mol for CDK‐2 compared with abemaciclib, palbociclib, and ribociclib drugs, indicating its potential as a potent CDK‐2 inhibitor.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.003 | 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 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".