A Simple Computational Tool for Accurate, Quantitative Prediction of One–Electron Reduction Potentials of Hypoxia–Activated Tirapazamine Analogues
Bibliographic record
Abstract
The reduction potentials of bioreductively-activated drugs represent an important design parameter to be accommodated in the course of creating lead compounds and improving the efficacy of older generation drugs. Reduction potentials are traditionally reported as single-electron reduction potentials, E(1), measured against reference electrodes under strictly defined experimental conditions. More recently, computational chemists have described redox properties in terms of a molecule's highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO), in electron volts (eV). The relative accessibility of HOMO/LUMO data through calculation using today's computer infrastructure and simplified algorithms make the calculated value (LUMO) attractive in comparison to the accepted but rigorous experimental determination of E(1). This paper describes the correlations of eV (LUMO) to E(1) for three series of bioreductively-activated benzotriazine di-N-oxides (BTDOs), ring-substituted BTDOs, ring-added BTDOs and a selection of aromatic nitro compounds. The current computational approach is a closed-shell calculation with a single optimization. Gas phase geometry optimization was followed by a single-point DFT (Density Functional Theory) energy calculation in the gas phase or in the presence of polar solvent. The resulting DFT-derived LUMO energies (eV) calculated for BTDO analogues in gas phase and in presence of polar solvent (water) exhibited very strong linear correlations with high computational efficiency (r2 = 0.9925) and a very high predictive ability (MAD = 7 mV and RMSD = 9 mV) when compared to reported experimentally determined single-electron reduction potentials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".