A Computational Approach towards Fragment Based Drug Design and Analysis Using G = (V, E) Decomposition
Bibliographic record
Abstract
Objectives: To develop an efficient algorithm for decomposing arbitrary graphs into circuits and paths, thereby enabling a more comprehensive analysis of molecules for fragment-based drug design. Methods: We have devised Algorithm GD for decomposing any graph into its constituent circuits and paths. A MATLAB implementation of this algorithm was developed to generate the necessary outputs. Algorithm GD was applied to identify nonoverlapping fragments within drug molecules. Results: The MATLAB code’s performance was evaluated in terms of sample outputs and runtime calculations. Algorithm GD was successfully employed to determine the non-overlapping fragments of fungicides. Subsequently, the Wiener Index of these fragments was calculated. Conclusion: A regression equation was established between the graph Wiener Index estimated from non-overlapping fragments and log KOC values. This model can be utilized to predict the log KOC values of fungicides without the need for experimental setups, thereby streamlining the drug discovery process.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".