A Computational Model for Overcoming Drug Resistance Using Selective Dual-Inhibitors for Aurora Kinase A and Its T217D Variant
Bibliographic record
Abstract
The human Aurora kinase-A (AK-A) is an essential mitotic regulator that is frequently overexpressed in several cancers. The recent development of several novel AK-A inhibitors has been driven by the well-established association of this target with cancer development and progression. However, resistance and cross-reactivity with similar kinases demands an improvement in our understanding of key molecular interactions between the Aurora kinase-A substrate binding pocket and potential inhibitors. Here, we describe the implementation of state-of-the-art virtual screening techniques to discover a novel set of Aurora kinase-A ligands that are predicted to strongly bind not only to the wild type protein, but also to the T217D mutation that exhibits resistance to existing inhibitors. Furthermore, a subset of these computationally screened ligands was shown to be more selective toward the mutant variant over the wild type protein. The description of these selective subsets of ligands provides a unique pharmacological tool for the design of new drug regimens aimed at overcoming both kinase cross-reactivity and drug resistance associated with the Aurora kinase-A T217D mutation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".