Evidence map›Paper›PMID 29445894›Full record

ReviewJournal of computer-aided molecular design2018

Sparse QSAR modelling methods for therapeutic and regenerative medicine.

David A Winkler

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of computer-aided molecular design, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. 3D-QSAR-aided toxicity assessment of synthetic musks and their transformation by-products.Environmental science and pollution research international · 2021
    Article
  3. Review
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

David A WinklerMonash Institute of Pharmaceutical Sciences, Monash University, Parkville, 3052, Australia. dave.winkler@csiro.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The quantitative structure-activity relationships method was popularized by Hansch and Fujita over 50 years ago. The usefulness of the method for drug design and development has been shown in the intervening years. As it was developed initially to elucidate which molecular properties modulated the relative potency of putative agrochemicals, and at a time when computing resources were scarce, there is much scope for applying modern mathematical methods to improve the QSAR method and to extending the general concept to the discovery and optimization of bioactive molecules and materials more broadly. I describe research over the past two decades where we have rebuilt the unit operations of the QSAR method using improved mathematical techniques, and have applied this valuable platform technology to new important areas of research and industry such as nanoscience, omics technologies, advanced materials, and regenerative medicine. This paper was presented as the 2017 ACS Herman Skolnik lecture.

Indexed as

Models, MolecularQuantitative Structure-Activity RelationshipBiocompatible MaterialsBone RegenerationComputers, MolecularDrug DesignHumansMachine LearningMolecular StructureNanostructuresProstheses and ImplantsRegenerative MedicineBiocompatible MaterialsDeep learningMachine learningQSARQuantitative structure–activity relationshipsRegenerative medicineSkolnik awardSparse feature selection

Identifiers

PMID29445894

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.