Pharmacophore Generation for Multiple Ligands

Handler, Norbert

doi:10.1002/9783527674381.ch10

Cited by 5 publications

(9 citation statements)

References 70 publications

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“…They represent a three-dimensional arrangement of features such as hydrogen bond donors/acceptors, charged residues, aromatic interactions, or lipophilic contacts. Moreover, excluded volumes may be defined in these three-dimensional models to avoid clashes with the biological target, leading to inactive compounds even though positive pharmacophore features are matched. − …”

Section: Multiple-targeting Ligand Identification and Optimizationmentioning

confidence: 99%

“…For example, small molecules can be aligned by superimposing single atoms, atom ensembles (fragments), or chemical/pharmacophore features with least-squares fitting, but also descriptor-based methods are available and modern alignment techniques use, e.g., pattern recognition. This large number of factors influencing the pharmacophore model generation in addition to the selection of training ligands can lead to remarkable variation in the final models and, therefore, ligand-based pharmacophore modeling is a challenging task even for a single target. ,− …”

Section: Multiple-targeting Ligand Identification and Optimizationmentioning

confidence: 99%

“…Structure-based generation of pharmacophore models provides the strong advantage that an optimal three-dimensional conformation of the ligand can be extracted from the co-crystal structure. ,− Moreover, pharmacophore features can be defined from the interactions of the ligand with a biological target and excluded volumes result from the binding site surface. However, structure-based pharmacophore models derived from a single co-crystal structure cannot cover the full chemical space of small molecules that might be interacting with the target of interest.…”

Section: Multiple-targeting Ligand Identification and Optimizationmentioning

confidence: 99%

“…However, structure-based pharmacophore models derived from a single co-crystal structure cannot cover the full chemical space of small molecules that might be interacting with the target of interest. A co-crystal structure represents only one possible “frozen” conformation of the protein and, thus, conformational adaption to a ligand is not reflected by a structure-based pharmacophore model …”

Section: Multiple-targeting Ligand Identification and Optimizationmentioning

confidence: 99%

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Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds

2018

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Multitargeting compounds comprising activity on more than a single biological target have gained remarkable relevance in drug discovery owing to the complexity of multifactorial diseases such as cancer, inflammation, or the metabolic syndrome. Polypharmacological drug profiles can produce additive or synergistic effects while reducing side effects and significantly contribute to the high therapeutic success of indispensable drugs such as aspirin. While their identification has long been the result of serendipity, medicinal chemistry now tends to design polypharmacology. Modern in vitro pharmacological methods and chemical probes allow a systematic search for rational target combinations and recent innovations in computational technologies, crystallography, or fragment-based design equip multitarget compound development with valuable tools. In this Perspective, we analyze the relevance of multiple ligands in drug discovery and the versatile toolbox to design polypharmacology. We conclude that despite some characteristic challenges remaining unresolved, designed polypharmacology holds enormous potential to secure future therapeutic innovation.

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Section: Multiple-targeting Ligand Identification and Optimizationmentioning

confidence: 99%

Section: Multiple-targeting Ligand Identification and Optimizationmentioning

confidence: 99%

Section: Multiple-targeting Ligand Identification and Optimizationmentioning

confidence: 99%

Section: Multiple-targeting Ligand Identification and Optimizationmentioning

confidence: 99%

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Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds

2018

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“…More recently, IUPAC gave an official definition as “an ensemble of steric and electronic features that is necessary to ensure the optimal supramolecular interactions with a specific biological target and to trigger (or block) its biological response”(Wermuth et al, 1998). There are two kinds of typical pharmacophore extraction methods, that is, ligand-based and structure-based (Handler and Buschmann, 2017; Machaba et al, 2017; Seidel et al, 2017). Structure-based virtual screening of pharmacophore has been developed as an important method for computer-aided drug design (Güner, 2002; Chen, 2013; Wang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

An Improved Receptor-Based Pharmacophore Generation Algorithm Guided by Atomic Chemical Characteristics and Hybridization Types

Gong

et al. 2018

Front. Pharmacol.

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Pharmacophore-based virtual screening is an important and leading compound discovery method. However, current pharmacophore generation algorithms suffer from difficulties, such as ligand-dependent computation and massive extractive chemical features. On the basis of the features extracted by the five probes in Pocket v.3, this paper presents an improved receptor-based pharmacophore generation algorithm guided by atomic chemical characteristics and hybridization types. The algorithm works under the constraint of receptor atom hybridization types and space distance. Four chemical characteristics (H-A, H-D, and positive and negative charges) were extracted using the hybridization type of receptor atoms, and the feature point sets were merged with 3 Å space constraints. Furthermore, on the basis of the original extraction of hydrophobic characteristics, extraction of aromatic ring chemical characteristics was achieved by counting the number of aromatics, searching for residual base aromatic ring, and determining the direction of aromatic rings. Accordingly, extraction of six kinds of chemical characteristics of the pharmacophore was achieved. In view of the pharmacophore characteristics, our algorithm was compared with the existing LigandScout algorithm. The results demonstrate that the pharmacophore possessing six chemical characteristics can be characterized using our algorithm, which features fewer pharmacophore characteristics and is ligand independent. The computation of many instances from the directory of useful decoy dataset show that the active molecules and decoy molecules can be effectively differentiated through the presented method in this paper.

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The Efficiency of Multi-target Drugs: A Network Approach

Alberca¹,

Talevi²

2020

Approaching Complex Diseases

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Pharmacophore Generation for Multiple Ligands

Cited by 5 publications

References 70 publications

Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds

Polypharmacology by Design: A Medicinal Chemist’s Perspective on Multitargeting Compounds

An Improved Receptor-Based Pharmacophore Generation Algorithm Guided by Atomic Chemical Characteristics and Hybridization Types

The Efficiency of Multi-target Drugs: A Network Approach

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