Machine Learning and Deep Learning Strategies in Drug Repositioning
Bibliographic record
Abstract
: Drug repositioning invovles exploring novel usages for existing drugs. It plays an important role in drug discovery, especially in the pre-clinical stages. Compared with the traditional drug discovery approaches, computational approaches can save time and reduce cost significantly. Since drug repositioning relies on existing drug-, disease-, and target-centric data, many machine learning (ML) approaches have been proposed to extract useful information from multiple data resources. Deep learning (DL) is a subset of ML and appears in drug repositioning much later than basic ML. Nevertheless, DL methods have shown great performance in predicting potential drugs in many studies. In this article, we review the commonly used basic ML and DL approaches in drug repositioning. Firstly, the related databases are introduced, while all of them are publicly available for researchers. Two types of preprocessing steps, calculating similarities and constructing networks based on those data, are discussed. Secondly, the basic ML and DL strategies are illustrated separately. Thirdly, we review the latest studies focused on the applications of basic ML and DL in identifying potential drugs through three paths: drug-disease associations, drug-drug interactions, and drug-target interactions. Finally, we discuss the limitations in current studies and suggest several directions of future work to address those limitations.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".