Computer aided study on cyclic tetrapeptide based ligands as potential inhibitors of Proplasmepsin IV
Bibliographic record
Abstract
The belief that we could always stay ahead of the pathogens was forced upon scientists in the whole world by antimicrobial resistance. According to several reports, there are medications that are yet to be made public in the pipeline and there are little motivations to design novel antimicrobials to combat the worldwide drug resistance issues. Presently, the desire to design and develop efficient novel anti-bacterial agents is very high by researchers; thus, this study focuses on identifying the interactions between the studied ligands and Proplasmepsin IV, as well as examining the relationship between the calculated descriptors and binding affinities. This work shows successful prediction of the reacting and inhibiting efficiency of ten (10) cyclic tetra-peptides using insilico method. The optimization of the studied compound revealed the proficiency of methyl (3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-9-(2-(methylthio)ethyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-3-carboxylate (F5) and 2-((3S,9S,12S)-12-(1,3-dioxoisoindolin-2-yl)-3-(methoxycarbonyl)-5,8,11-trioxo-4,7,10-triaza-1(1,3)-benzenacyclotridecaphane-9-yl)acetic acid (F7) to react more than the remaining molecules in term of HOMO and LUMO energies. In comparison, compound F9 demonstrated a higher inhibitory activity than the reference drug, Chloroquine, based on binding affinity. Molecular dynamics simulations over a 100 ns period further explored the binding affinity between F9 and the reference drug. The results showed that the reference drug (- 21.91 ± 1.16 kcal/mol) had a slightly stronger binding affinity than the F9_complex (- 13.85 ± 0.72 kcal/mol). Additionally, pharmacokinetic studies for F9 were compared with those of the reference compound and presented accordingly.
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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 | 0.000 | 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) | 0.004 | 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".