Synthesis of New Derivatives of Benzylidinemalononitrile and Ethyl 2-Cyano-3-phenylacrylate: In Silico Anticancer Evaluation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide In this study, microwave-assisted Knoevenagel condensation was used to produce two novel series of derivatives ( 1 – 6 ) from benzylidenemalononitrile and ethyl 2-cyano-3-phenylacrylate. The synthesized compounds were characterized using Fourier transform infrared (FT-IR) and 1 H NMR spectroscopies. The pharmacodynamics, toxicity profiles, and biological activities of the compounds were evaluated through an in silico study using prediction of activity spectra for substances (PASS) and Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) studies. According to the PASS prediction results, compounds 1 – 6 showed greater antineoplastic potency for breast cancer than other types of cancer. Molecular docking was employed to investigate the binding mode and interaction sites of the derivatives ( 1 – 6 ) with three human cancer targets (HER2, EGFR, and human FPPS), and the protein–ligand interactions of these derivatives were compared to those reference standards Tyrphostin 1 (AG9) and Tyrphostin 23 (A23). Compound 3 showed a stronger effect on two cell lines (HER2 and FPPS) than the reference drugs. A 20 ns molecular dynamics (MD) simulation was also conducted to examine the ligand’s behavior at the active binding site of the modeled protein, utilizing the lowest docking energy obtained from the molecular docking study. Enthalpies (Δ H ), Gibbs free energies (Δ G ), entropies (Δ S ), and frontier molecular orbital parameters (highest occupied molecular orbital–lowest unoccupied molecular orbital (HOMO–LUMO) gap, hardness, and softness) were calculated to confirm the thermodynamic stability of all derivatives. The consistent results obtained from the in silico studies suggest that compound 3 has potential as a new anticancer and antiparasitic drug. Further research is required to validate its efficacy.
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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) | 0.000 | 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) | 0.001 | 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".