Evidence map›Paper›PMID 38035191›Full record

ArticlePatterns (New York, N.Y.)2023

Optimal decision-making in high-throughput virtual screening pipelines.

Hyun-Myung Woo, Xiaoning Qian, Li Tan, Shantenu Jha, Francis J Alexander, Edward R Dougherty, Byung-Jun Yoon

Abstract read
In one paragraph

Article in Patterns (New York, N.Y.), 2023. 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. Article
  3. Article
  4. 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

7 authors.

Hyun-Myung WooDepartment of Biomedical & Robotics Engineering, Incheon National University, Incheon 22012, Republic of Korea.
Xiaoning QianDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Li TanComputational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA.
Shantenu JhaComputational Science Initiative, Brookhaven National Laboratory, Upton, NY 11973, USA.
Francis J AlexanderComputing, Environment and Life Sciences, Argonne National Laboratory, Lemont, IL 60439, USA.
Edward R DoughertyDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.
Byung-Jun YoonDepartment of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the enormous search space containing the candidates and the substantial computational cost of high-fidelity property prediction models make screening practically challenging. In this work, we propose a general framework for constructing and optimizing a high-throughput virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allocate the computational resources to models with varying costs and accuracy to optimize the return on computational investment. Based on both simulated and real-world data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate virtual screening without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.

Indexed as

high-throughput screeninghigh-throughput virtual screening pipelineHTSHTVSoptimal computational campaignoptimal decision-makingoptimal screeningreturn on computational investmentROCI

Identifiers

PMID38035191
PMCPMC10682755

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.