MétaCan
Menu
← Back to cohort
Record W3015170184 · doi:10.1101/2020.04.01.020206

Defining the Neural Kinome: Strategies and Opportunities for Small Molecule Drug Discovery to Target Neurodegenerative Diseases

2020· preprint· en· W3015170184 on OpenAlexaff
Andrea I. Krahn, Carrow I. Wells, David H. Drewry, Lenore K. Beitel, Thomas M. Durcan, Alison D. Axtman

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2020
Typepreprint
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsMcGill UniversityMontreal Neurological Institute and Hospital
Fundersnot available
KeywordsKinomeKinaseDrug discoverySmall moleculeDrug developmentDrugComputational biologyNeurosciencePharmacologyBiologyCancer researchBioinformaticsBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACT Kinases are highly tractable drug targets that have reached unparalleled success in fields such as cancer but whose potential has not yet been realized in neuroscience. There are currently 55 approved small molecule kinase-targeting drugs, 48 of which have an anti-cancer indication. The intrinsic complexity linked to central nervous system (CNS) drug development and a lack of validated targets has hindered progress in developing kinase inhibitors for CNS disorders when compared to other therapeutic areas such as oncology. Identification and/or characterization of new kinases as potential drug targets for neurodegenerative diseases will create opportunities for development of CNS drugs in the future. The track record of kinase inhibitors in other disease indications supports the idea that with the best targets identified small molecule kinase modulators will become impactful therapeutics for neurodegenerative diseases. KEY CONCEPTS Chemical probe : a high-quality small molecule that is potent, selective, and cell-active that meets the following criteria: (1) in vitro biochemical IC 50 < 50 nM, (2) ≥ 30-fold selectivity relative to other kinases in a large assay panel such as DiscoverX scanMAX , and (3) cellular activity or target engagement with an IC 50 < 1 μM Narrow spectrum : a selectivity threshold that can be defined as potently inhibiting ∼10% or less of all kinases screened Kinome : all human kinases Kinase chemogenomic set (KCGS) : publicly-available curated physical library of narrow spectrum and potent kinase inhibitors for which the SGC-UNC has received permission to share the compounds; subsequent releases will increase kinome-wide coverage Illuminating the Druggable Genome (IDG) program : several interconnected projects currently funded by the National Institutes of Health to provide information on historically understudied members within protein families that have provided drug targets; the three main focus areas are kinases, G-protein coupled receptors, and ion channels IDG kinase : a kinase that was nominated as dark (understudied) by the National Institutes of Health IDG program (curated list found here: https://druggablegenome.net/IDGProteinList ); IDG consortium members generate data and resources to aid in the illumination of the function of these kinases DK tool : a narrow spectrum inhibitor that exhibits a defined selectivity score (S 10 (1 μM) < 0.05) and cellular target engagement with an IC 50 < 1 μM; S 10 (1 μM) is a measure of selectivity equal to the percentage of screened kinases biochemically inhibited by >90% at 1 μM

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.041
GPT teacher head0.260
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venuebioRxiv (Cold Spring Harbor Laboratory)→Same topicComputational Drug Discovery Methods→French-language works237,207→