Chemistry‐driven Hit‐to‐lead Optimization Guided by Structure‐based Approaches

Hoffer, Laurent; Muller, Christophe; Roché, Philippe; Morelli, Xavier

doi:10.1002/minf.201800059

Cited by 40 publications

(34 citation statements)

References 86 publications

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“…Appropriate validation thus allows the user of such models to make an informed decision whether the model can be useful—or not—for a specific purpose. In the case of small‐molecule ligands complexed with macromolecules of interest, the goal is often identification of lead compounds [2], or in later stages, structure‐guided lead optimization [32]. Experimentally determined ligand structures are also used to train and evaluate docking and virtual ligand screening algorithms [33].…”

Section: Resultsmentioning

confidence: 99%

Ligand‐centered assessment of SARS‐CoV‐2 drug target models in the Protein Data Bank

et al. 2020

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A bright spot in the SARS‐CoV‐2 (CoV‐2) coronavirus pandemic has been the immediate mobilization of the biomedical community, working to develop treatments and vaccines for COVID‐19. Rational drug design against emerging threats depends on well‐established methodology, mainly utilizing X‐ray crystallography, to provide accurate structure models of the macromolecular drug targets and of their complexes with candidates for drug development. In the current crisis, the structural biological community has responded by presenting structure models of CoV‐2 proteins and depositing them in the Protein Data Bank (PDB), usually without time embargo and before publication. Since the structures from the first‐line research are produced in an accelerated mode, there is an elevated chance of mistakes and errors, with the ultimate risk of hindering, rather than speeding up, drug development. In the present work, we have used model‐validation metrics and examined the electron density maps for the deposited models of CoV‐2 proteins and a sample of related proteins available in the PDB as of April 1, 2020. We present these results with the aim of helping the biomedical community establish a better‐validated pool of data. The proteins are divided into groups according to their structure and function. In most cases, no major corrections were necessary. However, in several cases significant revisions in the functionally sensitive area of protein–inhibitor complexes or for bound ions justified correction, re‐refinement, and eventually reversioning in the PDB. The re‐refined coordinate files and a tool for facilitating model comparisons are available at https://covid-19.bioreproducibility.org. Database Validated models of CoV‐2 proteins are available in a dedicated, publicly accessible web service https://covid-19.bioreproducibility.org

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Section: Resultsmentioning

confidence: 99%

Ligand‐centered assessment of SARS‐CoV‐2 drug target models in the Protein Data Bank

et al. 2020

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“…Even though VS can also involve ligand-based approaches (e.g., based on pharmacophore mapping [4,5]), docking methods represent a remarkably productive (and informative) way to perform virtual screening (VS) campaigns and to find potential ligands for a given target [6,7]. By considering the relevance of the VS studies, it come as no surprise that many targeted procedures have been recently proposed to enhance the VS performances.…”

Section: Introductionmentioning

confidence: 99%

Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns

Pedretti

Mazzolari

Gervasoni

et al. 2019

IJMS

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The study proposes a novel consensus strategy based on linear combinations of different docking scores to be used in the evaluation of virtual screening campaigns. The consensus models are generated by applying the recently proposed Enrichment Factor Optimization (EFO) method, which develops the linear equations by exhaustively combining the available docking scores and by optimizing the resulting enrichment factors. The performances of such a consensus strategy were evaluated by simulating the entire Directory of Useful Decoys (DUD datasets). In detail, the poses were initially generated by the PLANTS docking program and then rescored by ReScore+ with and without the minimization of the complexes. The so calculated scores were then used to generate the mentioned consensus models including two or three different scoring functions. The reliability of the generated models was assessed by a per target validation as performed by default by the EFO approach. The encouraging performances of the here proposed consensus strategy are emphasized by the average increase of the 17% in the Top 1% enrichment factor (EF) values when comparing the single best score with the linear combination of three scores. Specifically, kinases offer a truly convincing demonstration of the efficacy of the here proposed consensus strategy since their Top 1% EF average ranges from 6.4 when using the single best performing primary score to 23.5 when linearly combining scoring functions. The beneficial effects of this consensus approach are clearly noticeable even when considering the entire DUD datasets as evidenced by the area under the curve (AUC) averages revealing a 14% increase when combining three scores. The reached AUC values compare very well with those reported in literature by an extended set of recent benchmarking studies and the three-variable models afford the highest AUC average.

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“…Fragments Optimized for Rapid Hit-to-Lead Chemistry Fragment-to-lead optimization is af requently encountered bottleneck in FBDD. [81] This can be ap articularly time-consuming endeavor if the elaboration chemistry has not been properly developed or the fragment hit is poorly optimizable with few handles available. Indeed, researchers at Astex Pharmaceuticals stress the need to have fragments optimized for rapid hit-to-lead chemistry and recommend establishing the elaboration chemistry prior to screening.…”

Section: Target-directed DCCmentioning

confidence: 99%

Library Design Strategies To Accelerate Fragment‐Based Drug Discovery

Troelsen

Clausen

2020

Chemistry A European J

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Fragment‐based drug discovery (FBDD) has become an established approach for the generation of early lead candidates. However, despite its success and inherent advantages, hit‐to‐candidate progression for FBDD is not necessarily faster than that of traditional high‐throughput screening. Thus, new technology‐driven library design strategies have emerged as a means to facilitate more efficient fragment screening and/or subsequent fragment‐to‐hit chemistry. This minireview discusses such strategies, which cover the use of labeled fragments for NMR spectroscopy, X‐ray crystallographic screening of specialized fragments, covalent linkage for mass spectrometry, dynamic combinatorial chemistry, and fragments optimized for easy elaboration.

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Chemistry‐driven Hit‐to‐lead Optimization Guided by Structure‐based Approaches

Cited by 40 publications

References 86 publications

Ligand‐centered assessment of SARS‐CoV‐2 drug target models in the Protein Data Bank

Ligand‐centered assessment of SARS‐CoV‐2 drug target models in the Protein Data Bank

Rescoring and Linearly Combining: A Highly Effective Consensus Strategy for Virtual Screening Campaigns

Library Design Strategies To Accelerate Fragment‐Based Drug Discovery

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