A First QSAR Model for Galectin-3 Glycomimetic Inhibitors Based on 3D Docked Structures
Why this work is in the frame
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Bibliographic record
Abstract
This study presents the first QSAR model for Galectin-3 glycomimetic inhibitors based on docked structures to the carbohydrate recognition domain (CRD). Quantitative numerical methods such as PLS (Partial Least Squares) and ANN (Artificial Neural Networks) have been used and compared on QSAR models to establish correlations between molecular properties and binding affinity values (Kd). Training and validation of QSAR predictive models was performed on a master dataset consisting of 136 compounds. The molecular structures and binding affinities (Kd) (136 compounds) were obtained from the literature. To address the issue of dimensionality reduction, molecular descriptors were selected with PLS contingency approach, ANN, PCA (Principal Component Analysis) and GA (Genetic Algorithms) for the best predictive Galectin-3 binding affinity (Kd). Final sets comprising 56, 31 and 35 descriptors were obtained with PLS, PCA and ANN, respectively. The objective of this prototype QSAR model is to serve as a first guideline for the design of novel and potent Gal-3 selective inhibitors with emphasis on modification at both C-3' and at O-3 positions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it