Role of Chemoinformatics and Machine Learning in Drug Repurposing
Bibliographic record
Abstract
Drug discovery is both a long and expensive process, characterized by low success rates and high costs of development. By identifying new therapeutic applications for existing drugs, drug repurposing represents another faster and less expensive alternative using safety profiles and pharmacokinetic data that are already established. Nevertheless, the drug repurposing field faces obstacles such as data scarcely integrated with one another, a lack of insight into molecular mechanisms, and difficulties in the integration of different types of such data. Chemoinformatics addresses the gaps of repurposing drug information by employing methods such as ligand- and structure-based virtual screening, molecular docking, and pharmacophore modeling. A number of tools are available for identifying drug–target interactions, making a shift toward a polypharmacological perspective. The use of three-dimensional molecular descriptors enables more accurate screening, mainly accounting for the molecular conformation and complex interactions. On the other hand, machine learning and deep learning, by using large amounts of data, help to predict drug–target interaction and new therapeutic uses on an unprecedented scale. Recent advances, such as AlphaFold for protein folding and more recently interaction prediction, increase the accuracy of drug repurposing while accelerating the candidate hit discovery timelines. In this review, we highlight several chemoinformatics and machine learning approaches used for different drug development-related tasks and discuss how these approaches can guide drug repurposing to tackle complex diseases and rapidly address emerging health crises.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".