Theoretical Study of Structures and Spectra of Small Anticancer Drugs: Fluorouracil, Hydroxyurea, and Tirapazamine
Bibliographic record
Abstract
Abstract The structures and spectra of anticancer drug molecules fluorouracil, hydroxyurea, and tirapazamine are studied with ab initio methods and density functional theory. We optimize the geometry of the three molecules in gas phase and compute the vibrational spectra, dipole moments, and static dipole polarizabilities. Based on the coupled cluster method with single and double excitations (CCSD) results as standard for comparison for the geometry of fluorouracil and hydroxyurea, we conclude that third‐order Moeller‐Plesset perturbation (MP3) theory is more reliable than its second‐order shortcut (MP2) or the popular method in density functional theory known as Becke three‐parameter Lee‐Yang‐Parr exchange‐correlation functional (B3LYP). Using the best methods based on past experience, we also calculate the vertical ionization energies of both valence and core electrons. Most of the results are new predictions, while others compare well with previous calculations and with available experimental data. On the other hand, the absorption spectra of the aqueous solution of three title molecules are studied with time‐dependent DFT using the polarizable continuum model in conjunction with the nonequilibrium solvation method. Out of over 30 exchange‐correlation functions/models, five are found to be more reliable than the others when compared with the observed UV/visible spectra of fluorouracil and tirapazmine.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".