MétaCan
Menu
Back to cohort

Drug Design for Alzheimer’s Disease: Biologics vs. Small Molecules

2023· review· en· W4392714786 on OpenAlexafffund
Donald F. Weaver

Bibliographic record

VenueCurrent Alzheimer Research · 2023
Typereview
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersKrembil Foundation
KeywordsMagic bulletPolypharmacyMAGIC (telescope)MedicineAlzheimer's diseaseDiseaseDrugSmall moleculePharmacologyNeurosciencePsychologyBioinformaticsChemistryBiologyInternal medicine

Abstract

fetched live from OpenAlex

There shall probably be no "magic bullet" for Alzheimer's; rather, we should be pursuing a "magic shotgun blast" that will target multiple complementary therapeutic receptors. Although protein misfolding/oligomerization will probably be one of these targets, this alone is insufficient and will require the co-administration of other therapeutic entities engaging targets, such as immunopathy, gliopathy, mitochondriopathy, synaptotoxicity or others. Although polypharmacy is emerging as the preferred therapeutic route, many questions remain unanswered. Should this be a cocktail of biologics, a concoction of small molecules, or a judicious combination of both? Biologics and small molecule drugs display both strengths and weaknesses. When addressing a disease as complex and globally important as Alzheimer's, there should be room for the continuing development of both of these therapeutic classes. Each has much to offer, and when used with their advantages and disadvantages in clear focus, an ultimate solution will probably require contributions from both.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Open science, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0060.003
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.719
GPT teacher head0.555
Teacher spread0.163 · 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 teacher head, not a consensus.

Study designOther design
Domainnot available
GenreReview

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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCurrent Alzheimer ResearchSame topicComputational Drug Discovery MethodsFrench-language works237,207