Predicting Proteasome Inhibition using Atomic Weighted Vector and Machine Learning
Bibliographic record
Abstract
Ubiquitin/Proteasome System (UPS) is a highly regulated mechanism of intracellular protein degradation and turnover. Through the concerted actions of a series of enzymes, proteins are marked for proteasomal degradation by being linked to the polypeptide co-factor, ubiquitin. The UPS participates in a wide array of biological functions such as antigen presentation, regulation of gene transcription and the cell cycle, and activation of NF-κB. Some researchers have applied QSAR method and machine learning in the study of proteasome inhibition (EC50(µmol/L)), such as: the analysis of proteasome inhibition prediction, in the prediction of multi-target inhibitors of UPP and in the prediction of protein contact map. Following this idea, we applied the new tool for obtaining molecular descriptor for modeling of proteasome Inhibition EC50 (µmol/L), in which has used this novel molecular descriptors (MDs) and different classification algorithms for these quantitative structure-activity relationship (QSAR) studies. In the present research, we use the Atomic Weighted Vector (AWV) as attributes with the objective to develop the QSAR modeling of this datasets and also compare a set of different machine learning (ML) techniques to solve this problem, such as: Linear Regression (LR), Multiple linear regression (MLR), Decision tree(DT), Regression Tree(RT), Random Forest(RF), M5P, K-nearest neighbors (IBK or kNN), Multi-Layer perceptron (MLP), Best-first search (BF) and Genetic Algorithm (GA). The figure shows the results of R2 of the ML-QSAR using ten- folds cross validation for 258 compounds. The results indicate that AWVs are very important tool for modeling the proteasome inhibitory regardless of the ML algorithm used. It can be suggested that the MD-AWV are suitable for codifying important structural information of the molecules and, thus, constitute an interesting alternative to building effective models for the prediction of the values of EC50 (µmol/L).
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".