In Silico-Guided Target Identification of a Scaffold-Focused Library:  1,3,5-Triazepan-2,6-diones as Novel Phospholipase A2 Inhibitors

Müller, Pascal; Lena, Gersande; Boilard, Éric; Bezzine, Sofiane; Lambeau, Gérard; Guichard, Gilles; Rognan, Didier

doi:10.1021/jm0606589

Cited by 68 publications

(56 citation statements)

References 58 publications

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“…[14][15][16][17] Likewise, structure-based approaches allowed also for detecting the target for some compounds in a focused library [18] and to discover three targets associated with constituents of a medicinal plant. [19] More recently, in silico target screening was used to suggest the targets against which selected compounds from a chemical library should be tested, leading to the identification of novel antagonists for all members of the adenosine receptor family.…”

Section: Introductionmentioning

confidence: 99%

In Silico Receptorome Screening of Antipsychotic Drugs

Vidal

Mestres²

2010

Molecular Informatics

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The recent availability of a complete interaction matrix between 13 antipsychotic drugs and 34 protein targets (Roth et al. Nat. Rev. Drug Discov. 2004, 3, 353-359) allows to assess the performance of computational methods on their ability to anticipate the entire affinity profile of drugs across multiple targets. The analyses reveal that our current implementations, based on the similarity of drugs against a reference set of small molecules for which pharmacological data is available in the public domain, are able to predict 65 % of the 442 affinities within 1-log unit error, with a level of precision above 92 %. In spite of the relatively small scale of this validation study, the results are indicative that in silico receptorome screening of drugs offers an efficient and cost-effective complement to in vitro screening.

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Section: Introductionmentioning

confidence: 99%

In Silico Receptorome Screening of Antipsychotic Drugs

Vidal

Mestres²

2010

Molecular Informatics

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“…Subsequent grouping into clusters of maximal diversity enabled the selection of one representative each from five groups, which were synthesised and tested in kinase inhibition assays, with two of the five synthesised compounds achieving micro-molar inhibition in five human kinases. In contrast to earlier inverse virtual screening approaches, where substantial numbers of compounds (26-265) needed to be synthesised (Muller et al, 2006;Urich et al, 2013), we present a case study of a highly economic and cost-effective pathway to the first lead molecules by putting less emphasis on the individual predicted docking poses but exploiting the statistical hit frequency. By synthesising just five compounds which were selected based on the results from panel docking, two compounds (9 and 10) with micro-molar IC 50 values were identified from the original library of 1235 members.…”

Section: Resultsmentioning

confidence: 99%

Panel docking of small-molecule libraries — Prospects to improve efficiency of lead compound discovery

Sarnpitak

Mujumdar

Taylor

et al. 2015

Biotechnology Advances

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Computational docking as a means to prioritise small molecules in drug discovery projects remains a highly popular in silico screening approach. Contemporary docking approaches without experimental parametrisation can reliably differentiate active and inactive chemotypes in a protein binding site, but the absence of a correlation between the score of a predicted binding pose and the biological activity of the molecule presents a clear limitation. Several novel or improved computational approaches have been developed in the recent past to aid in screening and profiling of small-molecule ligands for drug discovery, but also more broadly in developing conceptual relationships between different protein targets by chemical probing. Among those new methodologies is a strategy known as inverse virtual screening, which involves the docking of a compound into different protein structures. In the present article, we review the different computational screening methodologies that employ docking of atomic models, and, by means of a case study, present an approach that expands the inverse virtual screening concept. By computationally screening a reasonably sized library of 1235 compounds against a panel of 48 mostly human kinases, we have been able to identify five groups of putative lead compounds with substantial diversity when compared to each other. One representative of each of the five groups was synthesised, and tested in kinase inhibition assays, yielding two compounds with micro-molar inhibition in five human kinases. This highly economic and cost-effective methodology holds great promise for drug discovery projects, especially in cases where a group of target proteins share high structural similarity in their binding sites.

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“…Muller et al reported the identification of two secreted phospholipase A2 isoforms as targets of compounds sharing a 1,3,5-triazepan-2,6-dione scaffold by systematic docking of a small scaffold-focused combinatorial library to 2 100 sc-PDB structures. [48] A customized target selection flowchart using several filters (empirical docking score, target enrichment in the top 2 % scorers) was mainly responsible for this success.…”

Section: Success Storiesmentioning

confidence: 99%

Structure‐Based Approaches to Target Fishing and Ligand Profiling

Rognan¹

2010

Molecular Informatics

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111

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Chemogenomics is an emerging interdisciplinary field aiming at identifying all possible ligands of all possible targets. If one groups targets in columns and ligands in rows, chemogenomic approaches to drug discovery just fill the interaction matrix. Since experimental data do not suffice, several computational methods are currently actively developed to supplement time-consuming and costly experiments. They are either designed to fill rows and thus profile a ligand towards a heterogeneous set of targets (target profiling) or to fill columns and thus identify novel ligands for an existing target (standard virtual screening). At the interface of both strategies are now true chemogenomic computational methods filling well defined areas in the matrix. The present review will focus on (protein) structure-based approaches and illustrates major advances in this novel exciting field which is supposed to massively impact rational drug design in the next decade.

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In Silico-Guided Target Identification of a Scaffold-Focused Library: 1,3,5-Triazepan-2,6-diones as Novel Phospholipase A2 Inhibitors

Cited by 68 publications

References 58 publications

In Silico Receptorome Screening of Antipsychotic Drugs

In Silico Receptorome Screening of Antipsychotic Drugs

Panel docking of small-molecule libraries — Prospects to improve efficiency of lead compound discovery

Structure‐Based Approaches to Target Fishing and Ligand Profiling

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