Evidence map›Paper›PMID 34363562›Full record

ArticleJournal of computer-aided molecular design2021

Stacking Gaussian processes to improve

Robert M Raddi, Vincent A Voelz

Abstract read
PubMed Full text at PMC 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. Target discovery and drug design in the era of artificial intelligence.Medicinal chemistry research : an international journal for rapid communications on design and mechanisms of action of biologically active agents · 2026
    Review
  2. Article
  3. Article
  4. Stacking Gaussian processes to improveJournal of computer-aided molecular design · 2021
    Article
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

2 authors.

Robert M RaddiDepartment of Chemistry, Temple University, Philadelphia, PA 19122, USA.
Vincent A VoelzDepartment of Chemistry, Temple University, Philadelphia, PA 19122, USA.

Funding

Markov State Model approaches for folding, binding and designR01GM123296 · NIGMS · TEMPLE UNIV OF THE COMMONWEALTH · PI VOELZ, VINCENT · 2017 to 2025
$3.1M
Advancing predictive physical modeling through focused development of model systems to drive new modeling innovationsR01GM124270 · NIGMS · UNIVERSITY OF CALIFORNIA-IRVINE · PI MOBLEY, DAVID LOWELL · 2018 to 2021
$1.5M
NIGMS NIH HHS R01 GM123296NIGMS NIH HHS R01GM123296NIGMS NIH HHS R01 GM124270NIGMS NIH HHS R01GM124270
6 · The paper itself

Abstract

Accurate predictions of acid dissociation constants are essential to rational molecular design in the pharmaceutical industry and elsewhere. There has been much interest in developing new machine learning methods that can produce fast and accurate pKa predictions for arbitrary species, as well as estimates of prediction uncertainty. Previously, as part of the SAMPL6 community-wide blind challenge, Bannan et al. approached the problem of predicting

Indexed as

Machine LearningModels, ChemicalNormal DistributionSoftwareSolventsThermodynamicsSolventsAcid dissociation constantsComputational drug designGaussian process modelsMachine learningPhysicochemical propertiesSAMPL7 physical property prediction

Identifiers

PMID34363562
PMCPMC9478567

What OpenQuestion holds

Textmetadata
LicenceTDM
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.