Challenges and Perspectives in the Discovery of Dengue Virus EntryInhibitors
Bibliographic record
Abstract
Dengue virus (DENV) disease has become one of the major challenges in public health. Currently, there is no antiviral treatment for this infection. Since human transmission occurs via mosquitoes of the Aedes genus, most efforts have been focused on the control of this vector. However, these control strategies have not been totally successful, as reflected in the increasing number of DENV infections per year, becoming an endemic disease in more than 100 countries worldwide. Consequently, the development of a safe antiviral agent is urgently needed. In this sense, rational design approaches have been applied in the development of antiviral compounds that inhibit one or more steps in the viral replication cycle. The entry of viruses into host cells is an early and specific stage of infection. Targeting either viral components or cellular protein targets are an affordable and effective strategy for therapeutic intervention of viral infections. This review provides an extensive overview of the small organic molecules, peptides, and inorganic moieties that have been tested so far as DENV entry direct-acting antiviral agents. The latest advances based on computer-aided drug design (CADD) strategies and traditional medicinal chemistry approaches in the design and evaluation of DENV virus entry inhibitors will be discussed. Furthermore, physicochemical drug properties, such as solubility, lipophilicity, stability, and current results of pre-clinical and clinical studies will also be discussed in detail.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".