Molecular docking against Covid-19 and HIV, and the role of catalysis in stereoselective cycloaddition reactions: A theoretical investigation of TiCl4-promoted reactions between cyclopenta-1,3-diene and benzyl acrylate/benzyl 2-fluoroacrylate
Bibliographic record
Abstract
The study of cycloaddition reactions between cyclopenta-1,3-diene and benzyl-acrylate, as well as benzyl-2-fluoroacrylate with and without the catalyst (TiCl4), was conducted using MEDT. The results of the energy profiles suggest that these reactions are stereoselective, meaning that they favor the formation of certain stereoisomers over others. Furthermore, the addition of TiCl4 as a catalyst appears to enhance the selectivity of these reactions, which is in line with experimental observations. Additionally, a docking study was carried out to predict the effect of stereochemistry and the presence of specific atoms, such as fluorine, on their ability to bind to viral proteins responsible for SARS-Covid-19 and HIV. Moreover, the notable affinity of Ligand 4 for HIV renders it a pivotal contender for extended research, possibly paving the way for enhanced antiretroviral drugs. Similarly, the encouraging affinity demonstrated by Ligand 1 towards the Covid-19 protein highlights its promise for Covid-19 drug development.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".