Challenges in Integrative Research in Therapeutic and Diagnostic Proteins: Translation and Conformational Engineering
Bibliographic record
Abstract
Biopharmaceuticals are mostly protein-derived medical drugs play vital roles in the human body catalyzing sequence of biochemical reactions. These biologics, which include therapeutic proteins, monoclonal antibodies (MAbs), vaccines, hormones, and fusion proteins, are used in the treatment, diagnosis and prevention of specific diseases such as cancer, multiple sclerosis, rheumatoid arthritis, diabetes, and a variety of cardiovascular diseases [1-3]. They are large molecular weight compounds with complex 3D structures mimicking molecules found in human bodies. Their production by living cells makes them different from classical small molecular weight chemical drugs (e.g., antibiotics) and in general, protein drug production by living cells is generally difficult [3]. With the advent of recombinant DNA technology, the post genomic era is seeing a massive increase in valuable protein products entering the clinical trials to manage disease with high specificity [4]. The global market for recombinant bio-pharmaceuticals has been growing rapidly from 30 products with a market value of USD $50-60 billion in 2004 to more than 151 unique products approved by the FDA by 2012 valued at USD $138 billion and is expected to surpass $320 billion by 2020.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".