Bibliographic record
Abstract
Drug-induced blockade of human ether-a-go-go-related gene (hERG) remains a major impediment in delivering safe drugs to the market. Several drugs have been withdrawn from the market due to their severe cardiotoxic side effects triggered by their off-target interactions with hERG. Thus, identifying the potential hERG blockers at early stages of lead discovery is fast evolving as a standard in drug design and development. A number of in silico structure-based models of hERG have been developed as a low-cost solution to evaluate drugs for hERG liability, and it is now agreed that the hERG blockers bind at the large central cavity of the channel. Nevertheless, there is no clear convergence on the appropriate drug binding modes against the channel. The proposed binding modes differ in their orientations and interpretations on the role of key residues in the channel. Such ambiguities in the modes of binding remain to be a significant challenge in achieving efficient computational predictive models and in saving many important already Food and Drug Administration approved drugs. In this review, we discuss the spectrum of reported binding modes for hERG blockers, the various in silico models developed for predicting a drug's affinity to hERG, and the known successful optimization strategies to avoid off-target interactions with hERG.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".