Using Machine Learning Methods to Predict Experimental High Throughput Screening Data
Bibliographic record
Abstract
High throughput screening (HTS) remains a very costly process notwithstanding many recent technological advances in the field of biotechnology. In this study we consider the application of machine learning methods for predicting experimental HTS measurements. Such a virtual HTS analysis can be based on the results of real HTS campaigns carried out with similar compounds libraries and similar drug targets. In this way, we analyzed Test assay from McMaster University Data Mining and Docking Competition [1] using binary decision trees, neural networks, support vector machines (SVM), linear discriminant analysis, k-nearest neighbors and partial least squares. First, we studied separately the sets of molecular and atomic descriptors in order to establish which of them provides a better prediction. Then, the comparison of the six considered machine learning methods was made in terms of false positives and false negatives, methods sensitivity and enrichment factor. Finally, a variable selection procedure allowing one to improve the methods sensitivity was implemented and applied in the framework of polynomial SVM. Keywords: CART, decision trees, drug target, hit, k-nearest neighbors (kNN), linear discriminant analysis (LDA), neural networks (NN), partial least squares (PLS), ROC curve, sampling, support vector machines (SVM), virtual high throughput screening
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| 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) | 0.001 | 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".