Distance based algorithms for small biomolecule classification and structural similarity search
Bibliographic record
Abstract
MOTIVATION: 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 wrt 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. RESULTS: In this paper we focus on the k-nearest-neighbor search method, which, until recently was not considered for small molecule classification. The few recent applications of k-nn to compound classification focus on selecting the most relevant set of chemical descriptors which are then compared under standard Minkowski distance L(p). Here we show how to computationally design the optimal weighted Minkowski distance wL(p) for maximizing the discrimination between active and inactive compounds wrt bioactivities of interest. We then show how to construct pruning based k-nn search data structures for any wL(p) distance that minimizes similarity search time. The accuracy achieved by our classifier is better than the alternative LDA and MLR approaches and is comparable to the ANN methods. In terms of running time, our classifier is considerably faster than the ANN approach especially when large data sets are used. Furthermore, our classifier quantifies the level of bioactivity rather than returning a binary decision and thus is more informative than the ANN approach.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".