A new strategy based on drug-linker-nanocarrier interactions to design new HIV-1 non-nucleoside reverse transcriptase inhibitors
Bibliographic record
Abstract
The development of effective non-nucleoside inhibitors targeting HIV-1 reverse transcriptase (RT) remains a persistent challenge in AIDS research, particularly in overcoming drug-induced mutations. This study focuses on harnessing the potential of Rilpivirine (RPV), a widely recognized non-nucleoside inhibitor, as a foundational structure for designing and synthesizing inhibitors with superior anti-HIV-1 activities compared to RPV. Through strategic conjugation of RPV to molecular umbrellas using diverse linkers such as BSOCOES, DSP, and EGS, a novel series of potent non-nucleoside inhibitors is crafted. Guided by a structure-based drug design approach, this study unveils a new series of non-nucleoside reverse transcriptase inhibitors (NNRTIs). Comprehensive molecular analyses reveal extensive interactions between these NNRTIs and the RT inhibitor-binding pocket, confirming their superior antiviral efficacy against the wild-type virus when compared to RPVs. The innovative strategy employed in this research, focusing on drug-linker-nanocarrier interactions, introduces a promising avenue for designing and developing robust HIV-1 RT inhibitors with potential clinical applications. The findings emphasize the approach's potential for addressing challenges posed by drug-resistant mutations, opening new possibilities for advancing antiretroviral therapy.
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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 | 0.000 |
| 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) | 0.001 | 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".