Predicting Protein-Ligand Interactions Based on Chemical Preference Features with its Application to New D-Amino Acid Oxidase Inhibitor Discovery
Bibliographic record
Abstract
In silico prediction of the new drug-target interactions from existing databases is of important value for the drug discovery process. Currently, the amount of protein targets that have been identified experimentally is still very small compared with the entire human proteins. In order to predict protein-ligand interactions in an accurate manner, we have developed a support vector machine (SVM) model based on the chemical-protein interactions from STITCH. New features from ligand chemical space and interaction networks have been selected and encoded as the feature vectors for SVM analysis. Both the 5-fold cross validation and independent test show high predictive accuracy that outperforms the state-of-the-art method based on ligand similarity. Moreover, 91 distinct pairs of features have been selected to rebuild a simplifier model, which still maintains the same performance as that based on all 332 features. Then, this refined model is used to search for the potential D-amino acid oxidase inhibitors from STITCH database and the predicted results are finally validated by our wet experiments. Out of 10 candidates obtained, seven D-amino acid oxidase inhibitors have been verified, in which four are newly found in the present study, and one may have a new application in therapy of psychiatric disorders other than being an antineoplastic agent. Clearly, our model is capable of predicting potential new drugs or targets on a large scale with high efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".