Novel approaches for small biomolecule classification and structural similarity search
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Structural similarity search among small molecules is a standard tool used in molecular classification and in-silico drug discovery. The effectiveness of this general approach depends on how well the following problems are addressed. The notion of similarity should be chosen for providing the highest level of discrimination of compounds with respect to the bioactivity of interest. The data structure for performing search should be very efficient as the molecular databases of interest include several millions of compounds. In this paper we summarize the recent applications of k -nearest-neighbor search method for small molecule classification. The k -nn classification of small molecules is based on selecting the most relevant set of chemical descriptors which are then compared under standard Minkowski distance L p . Here we describe how to computationally design the optimal weighted Minkowski distance wL p for maximizing the discrimination between active and inactive compounds wrt bioactivities of interest. k -nn classification requires fast similarity search for predicting bioactivity of a new molecule. We then focus on construction of pruning based k -nn search data structures for any wL p distance that minimizes similarity search time. The accuracy achieved by k -nn classifier is better than the alternative LDA and MLR approaches and is comparable to the ANN methods. In terms of running time, k -nn classifier is considerably faster than the ANN approach especially when large data sets are used. Furthermore, k -nn classifier is capable of quantification of the level of bioactivity rather than returning a binary decision and can bring more insight to the nature of the activity via eliminating unrelated descriptors of the compounds with respect to the activity in question.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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