Analyzing compound activity records and promiscuity degrees in light of publication statistics

For the generation of contemporary databases of bioactive compounds, activity information is usually extracted from the scientific literature. However, when activity data are analyzed, source publications are typically no longer taken into consideration. Therefore, compound activity data selected from ChEMBL were traced back to thousands of original publications, activity records including compound, assay, and target information were systematically generated, and their distributions across the literature were determined. In addition, publications were categorized on the basis of activity records. Furthermore, compound promiscuity, defined as the ability of small molecules to specifically interact with multiple target proteins, was analyzed in light of publication statistics, thus adding another layer of information to promiscuity assessment. It was shown that the degree of compound promiscuity was not influenced by increasing numbers of source publications. Rather, most non-promiscuous as well as promiscuous compounds, regardless of their degree of promiscuity, originated from single publications, which emerged as a characteristic feature of the medicinal chemistry literature.

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Promiscuity progression of bioactive compounds over time

F1000Research ◽

10.12688/f1000research.6473.1 ◽

2015 ◽

Vol 4 ◽

pp. 118 ◽

Cited By ~ 8

Author(s):

Ye Hu ◽

Swarit Jasial ◽

Jürgen Bajorath

Keyword(s):

Bioactive Compounds ◽

Time Course ◽

Massive Data ◽

Activity Data ◽

Confidence Levels ◽

Constant Degree ◽

Compound Promiscuity ◽

History Of ◽

Low Degrees ◽

Over Time

In the context of polypharmacology, compound promiscuity is rationalized as the ability of small molecules to specifically interact with multiple targets. To study promiscuity progression of bioactive compounds in detail, nearly 1 million compounds and more than 5.2 million activity records were analyzed. Compound sets were assembled by applying different data confidence criteria and selecting compounds with activity histories over many years. On the basis of release dates, compounds and activity records were organized on a time course, which ultimately enabled monitoring data growth and promiscuity progression over nearly 40 years, beginning in 1976. Surprisingly low degrees of promiscuity were consistently detected for all compound sets and there were only small increases in promiscuity over time. In fact, most compounds had a constant degree of promiscuity, including compounds with an activity history of 10 or 20 years. Moreover, during periods of massive data growth, beginning in 2007, promiscuity degrees also remained constant or displayed only minor increases, depending on the activity data confidence levels. Considering high-confidence data, bioactive compounds currently interact with 1.5 targets on average, regardless of their origins, and display essentially constant degrees of promiscuity over time. Taken together, our findings provide expectation values for promiscuity progression and magnitudes among bioactive compounds as activity data further grow.

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High-resolution view of compound promiscuity

F1000Research ◽

10.12688/f1000research.2-144.v2 ◽

2013 ◽

Vol 2 ◽

pp. 144 ◽

Cited By ~ 18

Author(s):

Ye Hu ◽

Jürgen Bajorath

Keyword(s):

Drug Discovery ◽

High Resolution ◽

Small Molecule ◽

Therapeutic Efficacy ◽

Molecular Basis ◽

Activity Data ◽

Biological Targets ◽

Research Activities ◽

Compound Promiscuity ◽

Detailed Picture

Compound promiscuity is defined as the ability of a small molecule to specifically interact with multiple biological targets. So-defined promiscuity is relevant for drug discovery because it provides the molecular basis of polypharmacology, which is increasingly implicated in the therapeutic efficacy of drugs. Recent studies have analyzed different aspects of compound promiscuity on the basis of currently available activity data. In this commentary, we present take-home messages from these studies augmented with new results to generate a detailed picture of compound promiscuity that might serve as a reference for further discussions and research activities.

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Cell Morphology-Guided De Novo Hit Design by Conditioning Generative Adversarial Networks on Phenotypic Image Features

10.26434/chemrxiv.11594067.v1 ◽

2020 ◽

Author(s):

Oscar Méndez-Lucio ◽

Paula Andrea Marin Zapata ◽

Joerg Wichard ◽

David Rouquié ◽

Djork-Arné Clevert

Keyword(s):

Small Molecules ◽

De Novo ◽

Molecular Targets ◽

De Novo Design ◽

Image Features ◽

Mitigation Strategy ◽

Generative Adversarial Networks ◽

Target Information ◽

Adversarial Networks ◽

First Time

Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug discovery approach. We trained our model on a set of 30,000 compounds and their respective morphological profiles extracted from high content images; no target information was used to train the model. Using this approach, we were able to automatically design agonist-like compounds of different molecular targets.

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The MicroRNA Family Both in Normal Development and in Different Diseases: The miR-17-92 Cluster

BioMed Research International ◽

10.1155/2019/9450240 ◽

2019 ◽

Vol 2019 ◽

pp. 1-11 ◽

Cited By ~ 8

Author(s):

Xiaodan Bai ◽

Shengyu Hua ◽

Junping Zhang ◽

Shixin Xu

Keyword(s):

Small Molecules ◽

Target Genes ◽

Normal Development ◽

Neurological Diseases ◽

Multiple Target ◽

Cellular Processes ◽

Microrna Family ◽

Mirna Clusters ◽

And Function

An increasing number of research studies over recent years have focused on the function of microRNA (miRNA) molecules which have unique characteristics in terms of structure and function. They represent a class of endogenous noncoding single-strand small molecules. An abundance of miRNA clusters has been found in the genomes of various organisms often located in a polycistron. The miR-17-92 family is among the most famous miRNAs and has been identified as an oncogene. The functions of this cluster, together with the seven individual molecules that it comprises, are most related to cancers, so it would not be surprising that they are considered to have involvement in the development of tumors. The miR-17-92 cluster is therefore expected not only to be a tumor marker, but also to perform an important role in the early diagnosis of those diseases and possibly also be a target for tumor biotherapy. The miR-17-92 cluster affects the development of disease by regulating many related cellular processes and multiple target genes. Interestingly, it also has important roles that cannot be ignored in disease of the nervous system and circulation and modulates the growth and development of bone. Therefore, it provides new opportunities for disease prevention, clinical diagnosis, prognosis, and targeted therapy. Here we review the role of the miR-17-92 cluster that has received little attention in relation to neurological diseases, cardiac diseases, and the development of bone and tumors.

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Cell Morphology-Guided De Novo Hit Design by Conditioning Generative Adversarial Networks on Phenotypic Image Features

10.26434/chemrxiv.11594067 ◽

2020 ◽

Author(s):

Oscar Méndez-Lucio ◽

Paula Andrea Marin Zapata ◽

Joerg Wichard ◽

David Rouquié ◽

Djork-Arné Clevert

Keyword(s):

Small Molecules ◽

De Novo ◽

Molecular Targets ◽

De Novo Design ◽

Image Features ◽

Mitigation Strategy ◽

Generative Adversarial Networks ◽

Target Information ◽

Adversarial Networks ◽

First Time

Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug discovery approach. We trained our model on a set of 30,000 compounds and their respective morphological profiles extracted from high content images; no target information was used to train the model. Using this approach, we were able to automatically design agonist-like compounds of different molecular targets.

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A Review on the Obtaining of Functional Beers by Addition of Non-Cereal Adjuncts Rich in Antioxidant Compounds

Antioxidants ◽

10.3390/antiox10091332 ◽

2021 ◽

Vol 10 (9) ◽

pp. 1332

Author(s):

Rodrigo A. M. Paiva ◽

Yhan S. Mutz ◽

Carlos A. Conte-Junior

Keyword(s):

Phenolic Compounds ◽

Bioactive Compounds ◽

Scientific Literature ◽

Phenolic Content ◽

Health Claims ◽

Antioxidant Compounds ◽

Herbal Extracts ◽

Relevant Field ◽

Recent Trends ◽

Positive Increment

Beer is one of the oldest and most consumed beverages worldwide, and recent trends point to increased consumption of functional beers. However, there is a lack in the scientific literature on the effects of adding functional adjuncts in distinct steps of the manufacturing process and its implications on the final physicochemical and sensorial profile. Therefore, the present review analyzes the ingredients used and their insertion stage to achieve a functional beer with bioactive compounds, higher antioxidant activity, and improved sensory characteristics. The addition of fruits, herbal extracts, plants, and mushrooms in beers was documented. Furthermore, adjuncts were successfully added in wort boiling, fermentation, maturation, and packaging. The wort boiling step stands out among these four due to the superior extraction of phenolic compounds from the added adjuncts. On the other hand, adjunct addition in the maturation step induced low increases in antioxidant and phenolic content of the respective enriched beers. Fruits represented the majority of adopted adjuncts among the studies evaluated. Furthermore, the addition of fruits represented a positive increment in the beer’s volatile profile and an increase in sensory acceptability. A gap in the literature was found regarding the analysis of phenolic compounds with appropriate techniques such as HPLC-MS. Furthermore, there is a need to study the bioavailability of the incorporated bioactive compounds to prove the health claims inferred about these beers. In conclusion, functional beers are a little-explored relevant field, with potential for new studies.

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Symphyonema bifilamentata sp. nov., the Right Fischerella ambigua 108b: Half a Decade of Research on Taxonomy and Bioactive Compounds in New Light

Microorganisms ◽

10.3390/microorganisms9040745 ◽

2021 ◽

Vol 9 (4) ◽

pp. 745

Author(s):

Patrick Jung ◽

Paul M. D’Agostino ◽

Burkhard Büdel ◽

Michael Lakatos

Keyword(s):

Small Molecules ◽

Bioactive Compounds ◽

Bioactive Metabolites ◽

Phylogenetic Position ◽

Cyanobacterial Strain ◽

Protein Coding ◽

The Family ◽

True Branching Cyanobacteria ◽

The Right ◽

True Branching

Since 1965 a cyanobacterial strain termed ‘Fischerella ambigua 108b’ was the object of several studies investigating its potential as a resource for new bioactive compounds in several European institutes. Over decades these investigations uncovered several unique small molecules and their respective biosynthetic pathways, including the polychlorinated triphenyls of the ambigol family and the tjipanazoles. However, the true taxonomic character of the producing strain remained concealed until now. Applying a polyphasic approach considering the phylogenetic position based on the 16S rRNA and the protein coding gene rbcLX, secondary structures and morphological features, we present the strain ‘Fischerella ambigua 108b’ as Symphyonema bifilamentata sp. nov. 97.28. Although there is the type species (holotype) S. sinense C.-C. Jao 1944 there is no authentic living strain or material for genetic analyses for the genus Symphyonema available. Thus we suggest and provide an epitypification of S. bifilamentata sp. nov. 97.28 as a valid reference for the genus Symphyonema. Its affiliation to the family Symphyonemataceae sheds not only new light on this rare taxon but also on the classes of bioactive metabolites of these heterocytous and true-branching cyanobacteria which we report here. We show conclusively that the literature on the isolation of bioactive products from this organism provides further support for a clear distinction between the secondary metabolism of Symphyonema bifilamentata sp. nov. 97.28 compared to related and other taxa, pointing to the assignment of this organism into a separate genus.

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Scoring Ligand Efficiency: Potency, Ligand Efficiency and Product Ligand Efficiency within Big Data Landscape

Letters in Drug Design & Discovery ◽

10.2174/1570180816666190112154505 ◽

2019 ◽

Vol 16 (11) ◽

pp. 1258-1263 ◽

Cited By ~ 3

Author(s):

Jaroslaw Polanski ◽

Anna Pedrys ◽

Roksana Duszkiewicz ◽

Johann Gasteiger

Keyword(s):

Small Molecules ◽

Physical Meaning ◽

Inhibitory Concentration ◽

Molecular Descriptor ◽

Heavy Atom ◽

Complex Problem ◽

Ligand Efficiency ◽

Activity Data ◽

Drug Candidates ◽

Maximum Value

Background: Potency is the broadest available biological activity data type. In turn, Ligand Efficiency (LE) is a molecular descriptor that probes the ratio of potency vs Heavy Atom Count (HAC), which emphasizes low HAC more than potency and thus has drawbacks as an estimator of drug candidates. The objective was to design a novel transform to probe potency and HAC interaction in which potency and HAC would be balanced more evenly. Methods: In this study, potency data of ChEMBL, PubChem, FDA approvals and drug (fragments) were analysed. A novel descriptor, a product of the pAC50 value with HAC, multiplicative or Product Ligand Efficiency (PLE) was designed and tested. Results: In particular PLE was compared with pAC50 and LE vs the HAC statistics for different series of ligands. This indicated that PLE is an informative estimator that can be used to recognize the potential of drugs. PLE has a maximum value in the range around 30-50 HAC. Conclusion: Drug design is a complex problem. Similarly, to drug-likeness, LE prefers small molecules. This makes LE a tool serendipitously improving drug likeness. In this context, LE performs unexpectedly well even despite the uncertainty of its physical meaning. PLE is a more evenly balanced estimator whose physical meaning is the Minimum Inhibitory Concentration (MIC).

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Biological Activity Profiles of Multitarget Ligands from X-ray Structures

Molecules ◽

10.3390/molecules25040794 ◽

2020 ◽

Vol 25 (4) ◽

pp. 794

Author(s):

Christian Feldmann ◽

Jürgen Bajorath

Keyword(s):

Large Scale ◽

Pharmaceutical Research ◽

Data Consistency ◽

Exploratory Research ◽

Activity Data ◽

X Ray ◽

X Ray Crystallography ◽

Large Scale Analysis ◽

Compound Promiscuity ◽

Different Sources

In pharmaceutical research, compounds with multitarget activity receive increasing attention. Such promiscuous chemical entities are prime candidates for polypharmacology, but also prone to causing undesired side effects. In addition, understanding the molecular basis and magnitude of multitarget activity is a stimulating topic for exploratory research. Computationally, compound promiscuity can be estimated through large-scale analysis of activity data. To these ends, it is critically important to take data confidence criteria and data consistency across different sources into consideration. Especially the consistency aspect has thus far only been little investigated. Therefore, we have systematically determined activity annotations and profiles of known multitarget ligands (MTLs) on the basis of activity data from different sources. All MTLs used were confirmed by X-ray crystallography of complexes with multiple targets. One of the key questions underlying our analysis has been how MTLs act in biological screens. The results of our analysis revealed significant variations of MTL activity profiles originating from different data sources. Such variations must be carefully considered in promiscuity analysis. Our study raises awareness of these issues and provides guidance for large-scale activity data analysis.

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