Emerging Concepts of Guanine Nucleotide-Binding Protein-Coupled Receptor (GPCR) Function and Implications for High Throughput Screening
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Guanine nucleotide binding protein (G protein) coupled receptors (GPCRs) comprise one of the largest families of proteins in the human genome and are a target for 40% of all approved drugs. GPCRs have unique structural motifs that allow them to interact with a wide and diverse series of extracellular ligands, as well as intracellular proteins, G proteins, receptor activity-modifying proteins, arrestins, and indeed other receptors. This distinctive structure has led to numerous efforts to discover drugs against GPCRs with targeted therapeutic uses. Such "designer" drugs currently include allosteric regulators, inverse agonists, and drugs targeting hetero-oligomeric complexes. Moreover, the large family of orphan GPCRs provides a rich and novel field of targets to discover drugs with unique therapeutic properties. The numerous technologies to discover GPCR drugs have also greatly advanced over the years, facilitating compound screening against known and orphan GPCRs, as well as in the identification of unique designer GPCR drugs. Indeed, high throughput screening (HTS) technologies employing functional cell-based approaches are now widely used. These include measurement of second messenger accumulation such as cyclic AMP, calcium ions, and inositol phosphates, as well as mitogen-activated protein kinase activation, protein-protein interactions, and GPCR oligomerization. This review focuses on how the improved understanding of the molecular pharmacology of GPCRs, coupled with a plethora of novel HTS technologies, is leading to the discovery and development of an entirely new generation of GPCR-based therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it