Bibliographic record
Abstract
The potential areas of applications of chemogenomic approaches are very large. Thanks to the large amount of knowledge accumulated during years of research, it is now possible to consider the binding of a ligand to a protein in a much larger context. This knowledge combined with the augmentation of computing capabilities allows global approaches to investigate biological and pharmaceutical problems. Classification of proteins, focused libraries, selectivity profiles and elaboration of new ligands for orphan receptors can all be investigated using chemogenomic. G protein-coupled receptors (GPCRs) constitute a large protein family of significant interest in pharmaceutical research. Despite this interest, and excluding the more than 360 nonolfatory proteins, the endogenous ligands of about 100 GPCRs have still not been identified. The main limitation of GPCRs investigation is the lack of 3D structures. The goal of this review is to present different chemogenomic approaches that can be applied to GPCRs. Three types of such approaches are presented: ligand centered, protein centered and protein-ligand centered approaches. For each of them, current limitations and biases are mentioned. Keywords: Chemogenomic, computational chemistry, fingerprint, focus library, machine learning, gpcr, pharmacophore, augmentation, selectivity profiles and, G protein-coupled receptors, ligand centered, protein centered, protein-ligand centered approaches, biological assay, experimental binding assay
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".