Fragment-Based Design of Kinase Inhibitors: A Practical Guide

Erickson, Jon A.

doi:10.1007/978-1-4939-2486-8_13

Cited by 4 publications

(7 citation statements)

References 85 publications

(58 reference statements)

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“…Except in a limited number of cases, the ligand size itself does not have a strong impact on where the common substructure binds inside the protein ( Figure S5). This observation is consistent with fragment-based approaches where the molecules are usually built from a smaller fragment core and by definition this core is a part of the MCS for the grown ligands 18,29,30 .…”

Section: Validation Set Designsupporting

confidence: 86%

“…This idea is particularly useful in transferring binding mode information in target families, such as kinases. In fact, conservation of binding modes in kinases can be very helpful in fragment-based design methods . In POSIT, a docking program recently developed by OpenEye, an initial pose is generated based on shape or maximum common substructure (MCS) overlays to existing structures and followed by shape optimization or rigid docking.…”

Section: Introductionmentioning

confidence: 99%

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Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization

Gao¹,

Thorsteinson²,

Watson³

et al. 2015

J. Chem. Inf. Model.

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Accurately predicting how a small molecule binds to its target protein is an essential requirement for structure-based drug design (SBDD) efforts. In structurally enabled medicinal chemistry programs, binding pose prediction is often applied to ligands after a related compound's crystal structure bound to the target protein has been solved. In this article, we present an automated pose prediction protocol that makes extensive use of existing X-ray ligand information. It uses spatial restraints during docking based on maximum common substructure (MCS) overlap between candidate molecule and existing X-ray coordinates of the related compound. For a validation data set of 8784 docking runs, our protocol's pose prediction accuracy (80-82%) is almost two times higher than that of one unbiased docking method software (43%). To demonstrate the utility of this protocol in a project setting, we show its application in a chronological manner for a number of internal drug discovery efforts. The accuracy and applicability of this algorithm (>70% of cases) to medicinal chemistry efforts make this the approach of choice for pose prediction in lead optimization programs.

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Section: Validation Set Designsupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Knowledge-Based Strategy to Improve Ligand Pose Prediction Accuracy for Lead Optimization

Gao¹,

Thorsteinson²,

Watson³

et al. 2015

J. Chem. Inf. Model.

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“…68,78 ■ CONCLUSIONS FBDD strategies have proven very successful in the development of PDE inhibitors across the PDE gene superfamily, much akin to results seen for other gene families such as the protein kinases. 47,79,80 FBDD techniques can also be used to produce chemical probes targeting the PDEs, as well as lead compounds, and candidate drugs. The programs targeting PDEs described here demonstrate how initial fragment hits can be developed into promising and highly selective lead compounds.…”

Section: ■ Fragment-sized Pde Inhibitorsmentioning

confidence: 99%

“…The above examples show the versatility of fragment screening, both in terms of fragment library design and in the way in which information gained from the fragment screens can be used in drug discovery. FBDD approaches targeting the PDEs can be compared to earlier programs targeting the protein kinases, 47 from which it is clear that similar factors, namely, amenability to X-ray crystallization, relatively high potency of starting fragments, as well as direct transferability of structural knowledge from one related target to another within the extended protein family, drive success in both programs.…”

mentioning

confidence: 99%

Fragment-Based Drug Discovery of Phosphodiesterase Inhibitors

Svensson

Bender

Bailey³

2017

J. Med. Chem.

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Phosphodiesterases are proving to be fruitful targets for drug discovery. At the same time fragment-based drug discovery has matured into a powerful and widely applied technique. In this communication we review the application of fragment-based drug discovery for the successful identification of novel 3',5'-cyclic nucleotide phosphodiesterase (PDE) inhibitors, concentrating on both experimental and computational strategies for fragment screening and hit-to-lead development. To this end, we also mine the open access databases ChEMBL and PDB for fragments showing PDE inhibitory activity, as well as SureChEMBL for recent PDE related patents, to provide a wider context for exploring fragment diversity. Together these approaches form an integrated experimental and computational platform to exploit fragment-based drug discovery for this important gene superfamily.

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“…Many reviews have summarized the structural characteristics of kinase inhibitors, which showed that the binding of kinase inhibitors are dependent on the interactions occurring at several key residues in the ATP-binding pocket 10,13,14 . The highly conserved hinge loop linking the N-terminal lobe and C-terminal lobe of the kinase domain seems to be essential for the binding of kinase inhibitors.…”

Section: Introductionmentioning

confidence: 99%