Positional Adaptability in the Design of Mutation-Resistant Nonnucleoside HIV-1 Reverse Transcriptase Inhibitors: A Supramolecular Perspective
Bibliographic record
Abstract
Drug resistance is a key cause of failed treatment of HIV infection. The efficacy of nonnucleoside reverse transcriptase-inhibiting (NNRTI) drugs is impaired by the rapid emergence of drug-resistant mutations. The literature supports the idea that purposefully designed flexible NNRTIs at an active site may help overcome drug resistance. It is proposed here that the usual "lock and key" model, with respect to NNRTI drug design, be expanded to consider creating "master keys" that would automatically adjust conformations to fit all of the "locks" mutations may make. The present work introduces the novel perspective of designing and creating supramolecular assemblies as potential NNRTIs (instead of the relatively more rigid single-molecule inhibitors). Specifically, flexible self-assembling quinhydrone supramolecular dimers formed from quinonoid monomers (designed to be highly flexible NNRTIs themselves) will be offered as a working example of this new perspective in NNRTI drug design. Quinonoid compounds have demonstrated binding interactions at various sites of the HIV-1 RT enzyme, including the elusive ribonuclease H area. Quinhydrone self-organized dimers have at some point in their molecular architecture a noncovalently interacting donor-acceptor ring pair complex. This complex is at the heart of the increased torsional, rotational, and translational motion this species will experience at a particular active site. Flexible supramolecular assemblies, together with their flexible monomer components, may offer a critical advantage in retaining potency against a wide range of drug-resistant HIV-1 RTs. This new supramolecular perspective may also have broader implications in the general field of antimicrobial drug design.
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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 | 0.001 |
| 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".