Structure Driven Design of Novel Human Ether-A-Go-Go-Related-Gene Channel (hERG1) Activators
Bibliographic record
Abstract
One of the main culprits in modern drug discovery is apparent cardiotoxicity of many lead-candidates via inadvertent pharmacologic blockade of K+, Ca2+ and Na+ currents. Many drugs inadvertently block hERG1 leading to an acquired form of the Long QT syndrome and potentially lethal polymorphic ventricular tachycardia. An emerging strategy is to rely on interventions with a drug that may proactively activate hERG1 channels reducing cardiovascular risks. Small molecules-activators have a great potential for co-therapies where the risk of hERG-related QT prolongation is significant and rehabilitation of the drug is impractical. Although a number of hERG1 activators have been identified in the last decade, their binding sites, functional moieties responsible for channel activation and thus mechanism of action, have yet to be established. Here, we present a proof-of-principle study that combines de-novo drug design, molecular modeling, chemical synthesis with whole cell electrophysiology and Action Potential (AP) recordings in fetal mouse ventricular myocytes to establish basic chemical principles required for efficient activator of hERG1 channel. In order to minimize the likelihood that these molecules would also block the hERG1 channel they were computationally engineered to minimize interactions with known intra-cavitary drug binding sites. The combination of experimental and theoretical studies led to identification of functional elements (functional groups, flexibility) underlying efficiency of hERG1 activators targeting binding pocket located in the S4-S5 linker, as well as identified potential side-effects in this promising line of drugs, which was associated with multi-channel targeting of the developed 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".