Discovery of Potential Tyrosinase Inhibitors via Machine Learning and Molecular Docking with Experimental Validation of Activity and Skin Permeation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Tyrosinase, a copper-dependent oxidase, plays a critical role in melanin biosynthesis and is a target in skin-whitening cosmetics. Conventional inhibitors like arbutin and kojic acid are widely used but suffer from cytotoxicity, instability, and inconsistent efficacy, highlighting the need for safer, more effective alternatives. In this study, two ligand-based machine learning models were developed: one to predict the biological activity of compounds and the other to estimate specific pIC 50 values. These models were employed to screen potential tyrosinase inhibitors from natural product libraries and FDA-approved drug databases. Subsequently, the molecules identified through machine learning screening were subjected to more precise multi tyrosinase-like structures molecular docking to refine the selection. We identified three top-ranking inhibitors, rhodanine-3-propionic acid, lodoxamide, and cytidine 5′-(dihydrogen phosphate), with strong binding affinities mediated by metal ion coordination and π–π interactions at the enzyme’s active site. In vitro assays revealed that all three compounds exhibited higher inhibitory activity against mushroom tyrosinase compared to arbutin (IC 50 = 38.37 mM), with rhodanine-3-propionic acid displaying the most potent inhibition (IC 50 = 0.7349 mM). Furthermore, transdermal permeation experimental results confirmed that these compounds achieved markedly better skin permeability than commercial arbutin-based formulations, highlighting their potential as next-generation agents for inhibiting melanin production in cosmetic applications.
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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 | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".