Integrated Virtual Screening for Anti-Caries Compounds from Neem: Dual-Target Inhibition of Biofilm Formation and Bacterial DNA Replication
Bibliographic record
Abstract
Background: Dental caries arise from polymicrobial biofilms and require interventions that address both local virulence and systemic burden. Methods: A curated set of 124 neem-derived phytochemicals was screened against Streptococcus mutans glucansucrase (3AIC) and Staphylococcus aureus DNA gyrase B (3U2D) using harmonized AutoDock Vina parameters. Ligand standardization and receptor preparation followed conventional protocols. Results: The most favorable docking scores reached −10.7 kcal·mol−1 for 3AIC and −8.9 kcal·mol−1 for 3U2D. Redocking produced pose RMSD values of 1.52 Å (3AIC) and 0.96 Å (3U2D). Per-receptor ADMET profiles for the six top-ranked compounds indicated median logP values of 4.93 (3AIC) and 4.52 (3U2D), median TPSA values of 80.3 and 62.9 Å2, median rotatable bonds of 2.5 and 1.0, and median QED values of 0.41 and 0.76, respectively. Conclusions: An integrated, dual-target screen prioritized neem constituents with plausible local anti-cariogenic activity and physicochemical features compatible with systemic disposition. These in silico findings motivate targeted experimental validation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".