Evidence map›Paper›PMID 34716833›Full record

ArticleJournal of computer-aided molecular design2021

Imputation of sensory properties using deep learning.

Samar Mahmoud, Benedict Irwin, Dmitriy Chekmarev, Shyam Vyas, Jeff Kattas, Thomas Whitehead, Tamsin Mansley, Jack Bikker, Gareth Conduit, Matthew Segall

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of computer-aided molecular design, 2021. 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. Comparing massively-multitask regression algorithms for drug discovery.Journal of computer-aided molecular design · 2026
    Article
  2. 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

10 authors.

Samar MahmoudOptibrium Limited, Cambridge, UK. samar@optibrium.com.
Benedict IrwinOptibrium Limited, Cambridge, UK.
Dmitriy ChekmarevResearch and Development, International Flavors & Fragrances, Union Beach, NJ, USA.
Shyam VyasResearch and Development, International Flavors & Fragrances, Union Beach, NJ, USA.
Jeff KattasResearch and Development, International Flavors & Fragrances, Union Beach, NJ, USA.
Thomas WhiteheadIntellegens Limited, Cambridge, UK.
Tamsin MansleyOptibrium Limited, Cambridge, UK.
Jack BikkerResearch and Development, International Flavors & Fragrances, Union Beach, NJ, USA.
Gareth ConduitIntellegens Limited, Cambridge, UK.
Matthew SegallOptibrium Limited, Cambridge, UK.ORCID 0000-0002-2105-6535

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Predicting the sensory properties of compounds is challenging due to the subjective nature of the experimental measurements. This testing relies on a panel of human participants and is therefore also expensive and time-consuming. We describe the application of a state-of-the-art deep learning method, Alchemite™, to the imputation of sparse physicochemical and sensory data and compare the results with conventional quantitative structure-activity relationship methods and a multi-target graph convolutional neural network. The imputation model achieved a substantially higher accuracy of prediction, with improvements in R

Indexed as

Deep LearningAlgorithmsHumansQuantitative Structure-Activity RelationshipSensory Receptor CellsUncertaintyDeep learningImputationIn silico modelQuantitative structure–activity relationshipSensory properties

Identifiers

PMID34716833

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.