A large-scale computational approach to drug repositioning.
Bibliographic record
Abstract
We have developed a computational pipeline for the prediction of protein-small molecule interactions and have applied it to the drug repositioning problem through a large-scale analysis of known drug targets and small molecule drugs. Our pipeline combines forward and inverse docking, the latter of which is a twist on the conventional docking procedure used in drug discovery: instead of docking many compounds against a specific target to look for potential inhibitors, one compound is docked against many proteins to search for potential targets. We collected an extensive set of 1,055 approved small molecule drugs and 1,548 drug target binding pockets (representing 78 unique human protein therapeutic targets) and performed a large-scale docking using ICM software to both validate our method and predict novel protein-drug interactions. For the 37 known protein-drug interactions in our data set that have a known structure complex, all docked conformations were within 2.0A of the solved conformation, and 30 of these had a docking score passing the typical ICM score threshold. Out of the 237 known protein-drug interactions annotated by DrugBank, 74 passed the score threshold, and 52 showed the drug docking to another protein with a better docking score than to its known target. These protein targets are implicated in human diseases, so novel protein-drug interactions discovered represent potential novel indications for the drugs. Our results highlight the promising nature of the inverse docking method for identifying potential novel therapeutic uses for existing drugs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".