Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

Saldívar-González, Fernanda I.; Naveja, J. Jesús; Palomino-Hernández, Oscar; Medina-Franco, José L.

doi:10.1039/c6ra26230a

Cited by 27 publications

(13 citation statements)

References 37 publications

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“…Large and complex datasets from a phenotypic screen can often not be easily analyzed and therefore chemoinformatics approaches are often used to visualize and extract SAR [13] . Finally, we advocate aiming for in vivo confirmation of phenotype modulation as early as possible to avoid later failures of MoAs due to safety concerns or efficacy limitations.…”

Section: How To Validate Hits?mentioning

confidence: 99%

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Phenotypic Drug Discovery

et al. 2021

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Phenotypic drug discovery has a long track record of delivering innovative drugs and has received renewed attention in the last few years. The promise of this approach, however, comes with several challenges that should be addressed to avoid wasting time and resources on drugs with undesired modes of action or, worse, false‐positive hits. In this set of best practices, we go over the essential steps of phenotypic drug discovery and provide guidance on how to increase the chance of success in identifying validated and relevant chemical starting points for optimization: selecting the right assay, selecting the right compound screening library and developing appropriate hit validation assays. Then, we highlight the importance of initiating studies to determine the mode of action of the identified hits early and present the current state of the art.

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Section: How To Validate Hits?mentioning

confidence: 99%

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Phenotypic Drug Discovery

et al. 2021

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“…This technique was used to visualize the chemical space of the NP database BIOFACQUIM with the reference databases ChEMBL and NP assembled from the Universal Natural Products Database, the Natural Products Atlas, and Natural Products in PubChem Substance Database [ 29 ]. Visual representation of the chemical space is often used to systematically explore the structure-activity relationships (SAR) or structure multiple-activity relationships (SMARts) [ 73 ] of compound data sets and identify valuable “StARs” (structure-activity relationships) in chemical space [ 74 ].…”

Section: Chemoinformatic Profilingmentioning

confidence: 99%

Cheminformatics to Characterize Pharmacologically Active Natural Products

Medina-Franco

Saldívar-González

2020

Biomolecules

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Natural products have a significant role in drug discovery. Natural products have distinctive chemical structures that have contributed to identifying and developing drugs for different therapeutic areas. Moreover, natural products are significant sources of inspiration or starting points to develop new therapeutic agents. Natural products such as peptides and macrocycles, and other compounds with unique features represent attractive sources to address complex diseases. Computational approaches that use chemoinformatics and molecular modeling methods contribute to speed up natural product-based drug discovery. Several research groups have recently used computational methodologies to organize data, interpret results, generate and test hypotheses, filter large chemical databases before the experimental screening, and design experiments. This review discusses a broad range of chemoinformatics applications to support natural product-based drug discovery. We emphasize profiling natural product data sets in terms of diversity; complexity; acid/base; absorption, distribution, metabolism, excretion, and toxicity (ADME/Tox) properties; and fragment analysis. Novel techniques for the visual representation of the chemical space are also discussed.

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“…integrate information related to the physicochemical properties of compounds and their biological activities, in order to identify novel drug candidates [12][13][14]. Chemoinformatics has been applied to aid antiparasitic drug discovery [15][16][17], including the search for potential antileishmanial compounds through distinct approaches [18][19][20].…”

Section: Chemoinformatic Approaches Can Be Used To Analyze Andmentioning

confidence: 99%

Systematic search for benzimidazole compounds and derivatives with antileishmanial effects

et al. 2018

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Leishmaniasis is a neglected tropical disease that currently affects 12 million people, and over 1 billion people are at risk of infection. Current chemotherapeutic approaches used to treat this disease are unsatisfactory, and the limitations of these drugs highlight the necessity to develop treatments with improved efficacy and safety. To inform the rational design and development of more efficient therapies, the present study reports a chemoinformatic approach using the ChEMBL database to retrieve benzimidazole as a target scaffold. Our analysis revealed that a limited number of studies had investigated the antileishmanial effects of benzimidazoles. Among this limited number, L. major was the species most commonly used to evaluate the antileishmanial effects of these compounds, whereas L. amazonensis and L. braziliensis were used least often in the reported studies. The antileishmanial activities of benzimidazole derivatives were notably variable, a fact that may depend on the substitution pattern of the scaffold. In addition, we investigated the effects of a benzimidazole derivative on promastigotes and amastigotes of L. infantum and L. amazonensis using a novel fluorometric method. Significant antileishmanial effects were observed on both species, with L. amazonensis being the most sensitive. To the best of our knowledge, this chemoinformatic analysis represents the first attempt to determine the relevance of benzimidazole scaffolds for antileishmanial drug discovery using the ChEMBL database. The present findings will provide relevant information for future structure-activity relationship studies and for the investigation of benzimidazole-derived drugs as potential treatments for leishmaniasis.

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Getting SMARt in drug discovery: chemoinformatics approaches for mining structure–multiple activity relationships

Abstract: In light of the high relevance of polypharmacology, multi-target screening is a major trend in drug discovery.

Cited by 27 publications

References 37 publications

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Phenotypic Drug Discovery

The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Phenotypic Drug Discovery

Cheminformatics to Characterize Pharmacologically Active Natural Products

Systematic search for benzimidazole compounds and derivatives with antileishmanial effects

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