A Graph-Based Machine Learning Framework for Predicting Physicochemical Properties of Antiviral Drugs via Topological Indices
Bibliographic record
Abstract
This research develops a two-stage machine learning framework for predicting physicochemical properties of antiviral drugs through quantitative structure-property relationship (QSPR) modeling. We analyzed a diverse data set of 59 antiviral compounds, leveraging SMILES-based molecular descriptors to predict the first stage six topological indices: First Zagreb, Second Zagreb, ABC, Randic, Harmonic, and Forgotten. The models that provided predictions closest to the actual topological index values were employed in the second stage to estimate six physicochemical properties: molar refractivity, polar surface area, polarizability, molar volume, molecular weight, and complexity. This framework showed high predictive performance, achieving coefficient of determination of 0.9950 for molecular weight and 0.9891 for polarizability. Correlation analysis demonstrated strong relations between the topological indices and molecular properties, with the Randic index having the highest at 0.9969. A comparison with previous studies was conducted to evaluate the effectiveness of the proposed framework. This integrated pipeline provides an accurate, interpretable, and scalable framework for QSPR-based prediction of physicochemical properties in antiviral drugs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".