DFT, Molecular Docking, and ADME/Tox Screening Investigations of Market‑Available Drugs against SARS‑CoV‑2
Bibliographic record
Abstract
A series of drugs was investigated to determine structural, electronic and pharmacological properties, as well as the molecular affinity for the main protease of severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The drugs were submitted to density functional theory calculations to optimize structures and predict binding preferences. The optimized geometries were used in molecular docking simulations. In the docking study, the receiver was considered rigid and the drugs flexible. The Lamarckian genetic algorithm with global search and Pseudo-Solis and Wets with local search were adopted for docking. Absorption, distribution, metabolism, excretion and toxicological properties were obtained from the Pre-ADMET online server. In this series, the antiviral atazanavir showed the potential to inhibit the main protease of SARS‑CoV‑2, based on the free binding energy, inhibition constant, binding interactions and its favorable pharmacological properties. Therefore, we recommend carrying out further studies with in vitro tests and subsequent clinical tests to analyze its effectiveness in the treatment of SARS‑CoV‑2.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".