Evidence map›Paper›PMID 38852360›Full record

ArticleComputational biology and chemistry2024

Development of a novel representation of drug 3D structures and enhancement of the TSR-based method for probing drug and target interactions.

Tarikul I Milon, Yuhong Wang, Ryan L Fontenot, Poorya Khajouie, Francois Villinger, Vijay Raghavan, Wu Xu

Abstract read
In one paragraph

Article in Computational biology and chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
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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.

Tarikul I MilonDepartment of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
Yuhong WangNational Center for Advancing Translational Sciences, 9800 Medical Center Drive, Rockville, MD 20850, USA.
Ryan L FontenotDepartment of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA.
Poorya KhajouieDepartment of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA; The Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA.
Francois VillingerDepartment of Biology, University of Louisiana at Lafayette, New Iberia, LA 70560, USA.
Vijay RaghavanThe Center for Advanced Computer Studies, University of Louisiana at Lafayette, LA 70504, USA.
Wu XuDepartment of Chemistry, University of Louisiana at Lafayette, P.O. Box 44370, Lafayette, LA 70504, USA. Electronic address: wxx6941@louisiana.edu.

Funding

Development of a new TSR algorithm to represent sequences of proteins and 3D structures of nucleotides and molecular complexes for mechanistic understanding of protein sequence and structure relationsR15GM144944 · NIGMS · UNIVERSITY OF LOUISIANA AT LAFAYETTE · PI Wu Xu · 2021 to 2026
$963k
NIGMS NIH HHS R15 GM144944
6 · The paper itself

Abstract

Understanding the mechanisms underlying interactions between drugs and target proteins is critical for drug discovery. In our earlier studies, we introduced the Triangular Spatial Relationship (TSR)-based algorithm, which enables the representation of a protein's 3D structure as a vector of integers (TSR keys). These TSR keys correspond to substructures of the 3D structure of a protein and are computed based on the triangles constructed by all possible triples of C

Indexed as

AlgorithmsProteinsBinding SitesDrug DiscoveryLigandsMolecular StructurePharmaceutical PreparationsLigandsPharmaceutical PreparationsProteinsDrug 3D RepresentationDrug and Target InteractionDrug Binding SitePhosphorylated Amino AcidTSR-based Method

Identifiers

PMID38852360
PMCPMC11390338

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Registered trials

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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.