An Integrative Computational Approach for the Identification of C-Abl Kinase Inhibitors from Anti-Parkinson Plant-Derived Bioactive
Bibliographic record
Abstract
BACKGROUND: Oxidative stress is strongly linked to neurodegeneration through the activation of c-Abl kinase, which arrests α-synuclein proteolysis by interacting with parkin interacting substrate (PARIS) and aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2). This activation, triggered by ataxia-telangiectasia mutated (ATM) kinase, leads to dopaminergic neuron loss and α -synuclein aggregation, a critical pathophysiological aspect of Parkinson's disease (PD). To halt PD progression, pharmacological inhibition of c-Abl kinase is essential. Despite three generations of tyrosine kinase inhibitors (TKIs) being explored for PD treatment, they present significant concerns including poor blood-brain barrier penetration, off-target effects, and severe side effects. Notably, there are currently no FDA-approved c-Abl kinase inhibitors in clinical usage for PD treatment, highlighting the urgent need for potent, safe, and cost-effective alternatives. OBJECTIVE: This study aims to identify potential c-Abl kinase inhibitors from plant-derived compounds with reported anti-Parkinson's potential and their derivatives using molecular docking, molecular dynamics simulations (MDS), and in silico pharmacokinetics and toxicity profiling. METHODS: Seventy-eight compounds sourced from literature were docked against c-Abl kinase using Maestro 12.5. The top three hit compounds, along with nilotinib (control drug), were subjected to drug-likeness, ADMET profiling using the AI Drug Lab server and 100 ns MDS using Desmond. RESULTS: Amburoside A, diarylheptanoid MS13, and dimethylaminomethyl-substituted-curcumin showed binding affinities close to nilotinib, with values of -12.615, -12.556, and -11.895 kcal/mol respectively, compared to nilotinib's -16.826 kcal/mol. The three plant-derived compounds exhibited excellent structural stability and favorable ADMET profiles, including optimal blood-brain barrier permeation. CONCLUSION: experiments are necessary to validate these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".