Mechanistic and stereoelectronic insights into the asymmetric diels–alder (4+2) cycloaddition toward cantharidin derivatives via DFT: Computational elucidation of their anticancer potential through docking, ADMET, and molecular dynamics analysis
Bibliographic record
Abstract
This investigation employs Molecular Electron Density Theory (MEDT) to elucidate the stereoselective (4+2) cycloaddition between 1-(furan-2-yl)propan-1-one and 1-R-1H-pyrrole-2,5-dione, combining mechanistic and pharmacological analyses. DFT calculations at the M06/6-311++G(d,p) level identified six distinct reaction pathways, revealing the exo cycloadduct as the thermodynamically favored product, consistent with experimental observations. Transition state analysis through NCI revealed stabilizing CH-π and van der Waals interactions governing the exo preference. Beyond mechanistic insights, the derived norcantharimide analogues exhibit promising anticancer potential, with strong binding affinities against hematological malignancy targets. SwissADME profiling confirmed optimal drug-likeness (QED > 0.6, TPSA < 100 Ų) and low hepatotoxicity risk. Notably, molecular dynamics simulations established exceptional stability for lead compound P2d (RMSD < 2.0 Å over 100 ns), with persistent hydrogen bonding (occupancy > 75%) to key catalytic residues. These integrated computational results position these derivatives as viable candidates for blood cancer therapeutics, merging rigorous mechanistic understanding with preclinical potential assessment.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".