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Progress in the Rational Design for Polypharmacology Drug

2016· review· en· W2395635445 on OpenAlexaff
Jinming Zhou, Quanjie Li, Meng Wu, Chao Chen, Shan Cen

Bibliographic record

VenueCurrent Pharmaceutical Design · 2016
Typereview
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsMcGill Genome Centre
Fundersnot available
KeywordsRational designDrug discoveryDrug repositioningDrug designDrugComputer sciencePharmacologyBiologyBioinformatics

Abstract

fetched live from OpenAlex

BACKGROUND: Polypharmacology plays an important role in drug discovery. Polypharmacology drugs strategy provides a novel way in drug design. However, to develop a polypharmacology drug with desired profile remains a challenge. METHODS: Owing to the huge progress in computational biology and chemistry, the rational drug design is becoming increasingly important in discovery of polypharmacology drug. RESULTS: Several methodologies on the rational polypharmacology drug design have been developed, which are summarized and classified as ligand based design in polypharmacology, target based design in polypharmacology, and the hybrid of ligand and target based design in polypharmacology. CONCLUSION: We give an overview of the importance of polypharmacology in drug design and current trends in rational design of polypharmacology, which may be beneficial to the design and development of polypharmacology drugs.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.331
GPT teacher head0.521
Teacher spread0.190 · 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

Citations8
Published2016
Admission routes1
Has abstractyes

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