Applications of Current Proteomics Techniques in Modern Drug Design
Bibliographic record
Abstract
Proteins are currently the major drug targets and thus play a critical role in the process of modern drug design. This typically involves construction of drug compounds based on the structure of a drug target, validation for therapeutic efficacy of the drug compounds, evaluation of drug toxicity, and finally, clinical trial. Proteomics, defined as the comprehensive analysis of the proteins that are expressed in cells or tissues, can be employed at different stages of this process. Comparative proteomics can distinguish subtle changes in protein abundance at a depth of several thousand proteins at different conditions i.e. normal vs disease, to facilitate drug target identification. Also, chemical proteomics can be used to determine drug-target interactions and systematically analyze drug specificity and selectivity. Moreover, phosphoproteomics can be employed to monitor changes in phosphorylation events to characterize drug actions on cell signaling pathways. Similarly, functional proteomics can be utilized to investigate protein-protein and protein-ligand interactions for the clarification of the mechanism of drug action, identification of disease-related sub-networks and novel drug targets. Furthermore, quantitative proteomics can be used to characterize long-term drug effects on protein expression. In addition, computational approaches have emerged to convert complex proteomic data into sophisticated computer models of cellular protein networks. In this review, we will provide an overview of these state-of-the-art proteomics techniques, describe their underlying experimental concepts and compare them to each other, and discuss existing and future applications in the art of drug design and development.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".