Functional characterization of drug-protein interactions network
Bibliographic record
Abstract
Understanding the molecular mechanism that govern drug protein interactions is essential for efficient drug design. The progress in drug development is very slow compared to the rising variation in human genomes and the discovery of new diseases. Thus the need to have more efficient and effective d rug discovery pipelines is becoming essential. In this paper, we study and analyze the relationships between drugs and proteins that they target. We consider some properties of the proteins that can be used to give weight to protein-protein relationships. We aim to identify protein properties that might guide the drug to proteins. Amino acid enrichment in target clusters is analyzed to assess if certain drugs prefer particular amino acids. The correlation between the net charge of the drug with the amino acids in the target protein is studies as well. Moreover, characterizing the functional components in drug target clusters is necessary to find drug preference. Sequence motifs and domains, post-translational modification and biological pathways of target proteins are analyzed to understand drug preference. This characterizes the drug target proteins from sequence and functional angles. Finally, we realized the importance of the social network model in analyzing any problem that can be modeled as a network. Fortunately, the problem tackled in this paper fits well the network requirement of the social network model. Hence, we analyze the correlations using the social network model where actors are drugs and proteins; our aim is to analyze the relations between drugs and proteins by benefiting from the rich metrics developed to analyze social networks. In this paper, we briefly mention the social network technique in order to demonstrate its applicability which will be detailed in a future publication.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".