Diversity of small molecule HIV‐1 latency reversing agents identified in low‐ and high‐throughput small molecule screens
Bibliographic record
Abstract
The latency phenomenon produced by human immunodeficiency virus (HIV-1) prevents viral clearance by current therapies, and consequently development of a cure for HIV-1 disease represents a formidable challenge. Research over the past decade has resulted in identification of small molecules that are capable of exposing HIV-1 latent reservoirs, by reactivation of viral transcription, which is intended to render these infected cells sensitive to elimination by immune defense recognition or apoptosis. Molecules with this capability, known as latency-reversing agents (LRAs) could lead to realization of proposed HIV-1 cure strategies collectively termed "shock and kill," which are intended to eliminate the latently infected population by forced reactivation of virus replication in combination with additional interventions that enhance killing by the immune system or virus-mediated apoptosis. Here, we review efforts to discover novel LRAs via low- and high-throughput small molecule screens, and summarize characteristics and biochemical properties of chemical structures with this activity. We expect this analysis will provide insight toward further research into optimized designs for new classes of more potent LRAs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".