Evidence map›Paper›PMID 29987709›Full record

ArticleJournal of computer-aided molecular design2018

Conformational ensemble comparison for small molecules in drug discovery.

Matthew Habgood

Abstract read
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In one paragraph

Article 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. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Matthew HabgoodEvotec (UK) Ltd., 114 Innovation Drive, Milton Park, Abingdon, Oxfordshire, OX14 4RZ, UK. matthew.habgood@evotec.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Quantification of three-dimensional similarity between small molecules is a fundamental tool of rational drug design. However, there are no widely-adopted scoring approaches for comparing whole conformational ensembles between molecules. Such scores would be desirable for scenarios in which properties of a molecule have been measured (e.g. activity against a target) but the relevant three dimensional structure is not known. In this study, a set of three complementary ensemble comparison scores is proposed. These are the maximum similarity between any pair of conformations; the proportion of the whole set of the conformations that are matched to within a threshold 3D similarity score; and the average value over these matched conformations of the molecular shape descriptor 'σ-fct', introduced by Ballester et al. The utility of this scoring set is demonstrated in three case studies. The first is an attempt to discriminate between the conformational behaviours of a series of compounds with varying types of cyclisations and other conformationally-significant modifications; the second is an analysis of the more and less active members of a series of GPR119 agonists plus an analysis of a series of orexin-1 antagonists; and the third case study is an attempt to obtain enrichment of active against inactive compounds for a subset of the DUD·E dataset, by ensemble comparison against an active reference compound.

Indexed as

Models, MolecularAlgorithmsAnimalsDatabases, ChemicalDrug DiscoveryLigandsMolecular ConformationOrexin Receptor AntagonistsOrexin ReceptorsOrganic ChemicalsRatsReceptors, G-Protein-CoupledResearch DesignStructure-Activity RelationshipGPR119 protein, ratLigandsOrexin Receptor AntagonistsOrexin ReceptorsOrganic ChemicalsReceptors, G-Protein-Coupled3D structural similarity4D structural similarityBioactive conformationComputer-aided drug designConformersLigand-based drug designMOEROCSVirtual screening

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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.