scholarly journals Maximum Common Property: A New Approach for Molecular Similarity

2020 ◽  
Author(s):  
Gonzalo Cerruela-García ◽  
Aurelio Antelo Collado ◽  
Ramón Carrasco-Velar ◽  
Nicolás García-Pedrajas
Keyword(s):  
Common Property ◽  
Antimalarial Activity ◽  
Biological Response ◽  
Chemical Compounds ◽  
Molecular Fragments ◽  
Property A ◽  
New Approach ◽  
Chemical Structures ◽  
Common Substructure ◽  
State Index

Abstract The maximum common property similarity (MCPhd) method is presented using descriptors as a new approach to determine the similarity between two chemical compounds or molecular graphs. This method uses the concept of maximum common property arising from the concept of maximum common substructure and is based on the electrotopographic state index for atoms. A new algorithm to quantify the similarity values of chemical structures based on the presented maximum common property concept is also developed in this paper. To verify the validity of this approach, the similarity of a sample of compounds with antimalarial activity is calculated and compared with the results obtained by the small molecule subgraph detector (SMSD) method. The results obtained by the MCPhd method differ significantly from those obtained by the SMSD method, improving the quantification of the similarity. A major advantage of the proposed method is that it helps to understand the analogy or proximity between physicochemical properties of the molecular fragments or subgraphs compared with the biological response or biological activity. In this new approach, more than one property can be potentially used. The method can be considered a hybrid procedure because it combines descriptor and the fragment approaches.

scholarly journals Maximum common property: a new approach for molecular similarity

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Aurelio Antelo-Collado ◽  
Ramón Carrasco-Velar ◽  
Nicolás García-Pedrajas ◽  
Gonzalo Cerruela-García
Keyword(s):  
Common Property ◽  
Antimalarial Activity ◽  
Biological Response ◽  
Property A ◽  
Molecular Fingerprint ◽  
New Approach ◽  
Chemical Structures ◽  
Feature Similarity ◽  
Common Substructure ◽  
State Index

Abstract The maximum common property similarity (MCPhd) method is presented using descriptors as a new approach to determine the similarity between two chemical compounds or molecular graphs. This method uses the concept of maximum common property arising from the concept of maximum common substructure and is based on the electrotopographic state index for atoms. A new algorithm to quantify the similarity values of chemical structures based on the presented maximum common property concept is also developed in this paper. To verify the validity of this approach, the similarity of a sample of compounds with antimalarial activity is calculated and compared with the results obtained by four different similarity methods: the small molecule subgraph detector (SMSD), molecular fingerprint based (OBabel_FP2), ISIDA descriptors and shape-feature similarity (SHAFTS). The results obtained by the MCPhd method differ significantly from those obtained by the compared methods, improving the quantification of the similarity. A major advantage of the proposed method is that it helps to understand the analogy or proximity between physicochemical properties of the molecular fragments or subgraphs compared with the biological response or biological activity. In this new approach, more than one property can be potentially used. The method can be considered a hybrid procedure because it combines descriptor and the fragment approaches.

scholarly journals Predicting biological pathways of chemical compounds with a profile-inspired aproach

2021 ◽  
Author(s):  
Javier Lopez-Ibañez ◽  
Florencio Pazos ◽  
Monica Chagoyen
Keyword(s):  
Protein Sequence ◽  
Chemical Compounds ◽  
Biological Pathways ◽  
Biological Pathway ◽  
Molecular Fragments ◽  
New Members ◽  
Chemical Structures ◽  
Functional Features ◽  
Sequence Profiles ◽  
The Relationship

AbstractAssignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites (those in the same biological pathway), based on a fragment-based vectorial representation of their chemical structures. We use this representation to predict the biological pathway of a chemical compound with good overall accuracy (AUC 0.74-0.90 depending on the database tested), and analyzed some factors that affect performance. The approach, which is compared with equivalent methods, can in addition detect those molecular fragments characteristic of a pathway. The method is available as a graphical interactive web server http://csbg.cnb.csic.es/iFragMent

scholarly journals Predicting biological pathways of chemical compounds with a profile-inspired approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Javier Lopez-Ibañez ◽  
Florencio Pazos ◽  
Monica Chagoyen
Keyword(s):  
Protein Sequence ◽  
Chemical Compounds ◽  
Biological Pathways ◽  
Biological Pathway ◽  
Molecular Fragments ◽  
New Members ◽  
Chemical Structures ◽  
Functional Features ◽  
Sequence Profiles ◽  
The Relationship

Abstract Background Assignment of chemical compounds to biological pathways is a crucial step to understand the relationship between the chemical repertory of an organism and its biology. Protein sequence profiles are very successful in capturing the main structural and functional features of a protein family, and can be used to assign new members to it based on matching of their sequences against these profiles. In this work, we extend this idea to chemical compounds, constructing a profile-inspired model for a set of related metabolites (those in the same biological pathway), based on a fragment-based vectorial representation of their chemical structures. Results We use this representation to predict the biological pathway of a chemical compound with good overall accuracy (AUC 0.74–0.90 depending on the database tested), and analyzed some factors that affect performance. The approach, which is compared with equivalent methods, can in addition detect those molecular fragments characteristic of a pathway. Conclusions The method is available as a graphical interactive web server http://csbg.cnb.csic.es/iFragMent.

Therapeutic Properties of Several Chemical Compounds from Salvia officinalis L. in Alzheimer’s Disease

2020 ◽  
Vol 21 ◽  
Author(s):  
Georgiana Uță ◽  
Denisa Ștefania Manolescu ◽  
Speranța Avram
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Side Effects ◽  
Chemical Compounds ◽  
Natural Compounds ◽  
Salvia Officinalis ◽  
Chemical Structures ◽  
In Silico Studies ◽  
Wide Range ◽  
Salvia Officinalis L

Background.: Currently, the pharmacological management in Alzheimer's disease is based on several chemical structures, represented by acetylcholinesterase and N-methyl-D-aspartate (NMDA) receptor ligands, with still unclear molecular mechanisms, but severe side effects. For this reason, a challenge for Alzheimer's disease treatment remains to identify new drugs with reduced side effects. Recently, the natural compounds, in particular certain chemical compounds identified in the essential oil of peppermint, sage, grapes, sea buckthorn, have increased interest as possible therapeutics. Objectives.: In this paper, we have summarized data from the recent literature, on several chemical compounds extracted from Salvia officinalis L., with therapeutic potential in Alzheimer's disease. Methods.: In addition to the wide range of experimental methods performed in vivo and in vitro, also we presented some in silico studies of medicinal compounds. Results. Through this mini-review, we present the latest information regarding the therapeutic characteristics of natural compounds isolated from Salvia officinalis L. in Alzheimer's disease. Conclusion.: Thus, based on the information presented, we can say that phytotherapy is a reliable therapeutic method in a neurodegenerative disease.

Topological characterization of dendrimer, benzenoid, and nanocone

2018 ◽  
Vol 74 (1-2) ◽  
pp. 35-43
Author(s):  
Wei Gao ◽  
Muhammad Kamran Siddiqui ◽  
Najma Abdul Rehman ◽  
Mehwish Hussain Muhammad
Keyword(s):  
Chemical Properties ◽  
Chemical Compounds ◽  
Topological Indices ◽  
Zagreb Index ◽  
Topological Characterization ◽  
Complex Molecules ◽  
Chemical Structures ◽  
Physico Chemical ◽  
Quantitative Structure Activity Relationships

Abstract Dendrimers are large and complex molecules with very well defined chemical structures. More importantly, dendrimers are highly branched organic macromolecules with successive layers or generations of branch units surrounding a central core. Topological indices are numbers associated with molecular graphs for the purpose of allowing quantitative structure-activity relationships. These topological indices correlate certain physico-chemical properties such as the boiling point, stability, strain energy, and others, of chemical compounds. In this article, we determine hyper-Zagreb index, first multiple Zagreb index, second multiple Zagreb index, and Zagreb polynomials for hetrofunctional dendrimers, triangular benzenoids, and nanocones.

scholarly journals Ethnobotanical, phytochemical and pharmacological aspects of Bengal Pogostemon (Pogostemon benghalensis)

2020 ◽  
Vol 9 (4) ◽  
pp. 318-327
Author(s):  
Sangeeta Dahiya ◽  
Daizy R. Batish ◽  
Harminader Pal Singh
Keyword(s):  
Molecular Level ◽  
Antioxidant Activities ◽  
Chemical Compounds ◽  
Biologically Active ◽  
Pharmacological Activities ◽  
Plant Parts ◽  
Chemical Structures ◽  
Traditional Uses ◽  
Traditional Remedies ◽  
Pharmacological Studies

Pogostemon benghalensis (Burm.f.) Kuntze (Lamiaceae) is an important aromatic plant. Multiple classes of phytochemicals such as flavonoids, phenols, phytosteroids, carbohydrates, fatty acids, glycosides, sterols, terpenoids, tannins, essential oil, and alkaloids have been isolated from the title species. Different plant parts have been used as traditional remedies for various ailments. The present review aims to update and coherent the fragmented information on botanical aspects, phytochemistry, traditional uses, and pharmacological activities. An extensive review of the literature was carried out by using various search engines like PubMed, Scopus, Science Direct, Google Scholar, Google, Scifinder for information. The articles were searched using the keywords "Pogostemon", "Parviflorus’, "benghalensis". Chemical structures of the chemical compounds were drawn using software Chem Draw ultra 8.0. Most of the plant parts have been used for the treatment of various ailments. Phytochemistry reveals that the plant is a rich source of various biologically active compounds. Pogostemon extracts exhibited numerous pharmacological effects like anticancer, anti-inflammatory, antimicrobial and antioxidant activities. In sum, P. benghalensis is a promising aromatic and medicinal plant as depicted by its various traditional uses and pharmacological studies. Bioactive compounds, responsible for its various pharmacological activities at the molecular level, need further detailed investigations. Future clinical studies are also required to validate the various traditional uses of P. benghalensis.

scholarly journals MMHub, a database for the mulberry metabolome

Database ◽  
2020 ◽  
Vol 2020 ◽  
Author(s):  
Dong Li ◽  
Bi Ma ◽  
Xiaofei Xu ◽  
Guo Chen ◽  
Tian Li ◽  
...  
Keyword(s):  
Mass Spectra ◽  
Chemical Compounds ◽  
Bioactive Metabolites ◽  
Molecular Formula ◽  
Experimental Conditions ◽  
Chemical Structures ◽  
Pubchem Compound ◽  
Specific Distribution ◽  
Small Chemical ◽  
Metabolome Data

Abstract Mulberry is an important economic crop plant and traditional medicine. It contains a huge array of bioactive metabolites such as flavonoids, amino acids, alkaloids and vitamins. Consequently, mulberry has received increasing attention in recent years. MMHub (version 1.0) is the first open public repository of mass spectra of small chemical compounds (<1000 Da) in mulberry leaves. The database contains 936 electrospray ionization tandem mass spectrometry (ESI-MS2) data and lists the specific distribution of compounds in 91 mulberry resources with two biological duplicates. ESI-MS2 data were obtained under non-standardized and independent experimental conditions. In total, 124 metabolites were identified or tentatively annotated and details of 90 metabolites with associated chemical structures have been deposited in the database. Supporting information such as PubChem compound information, molecular formula and metabolite classification are also provided in the MS2 spectral tag library. The MMHub provides important and comprehensive metabolome data for scientists working with mulberry. This information will be useful for the screening of quality resources and specific metabolites of mulberry. Database URL: https://biodb.swu.edu.cn/mmdb/

scholarly journals M-Polynomial and Degree Based Topological Indices of Some Nanostructures

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 831 ◽  
Author(s):  
Zahid Raza ◽  
Mark Essa K. Sukaiti
Keyword(s):  
Graphical Representation ◽  
Chemical Properties ◽  
Chemical Compounds ◽  
Detailed Comparison ◽  
Topological Indices ◽  
Chemical Structures

The association of M-polynomial to chemical compounds and chemical networks is a relatively new idea, and it gives good results about the topological indices. These results are then used to correlate the chemical compounds and chemical networks with their chemical properties and bioactivities. In this paper, an effort is made to compute the general form of the M-polynomials for two classes of dendrimer nanostars and four types of nanotubes. These nanotubes have very nice symmetries in their structural representations, which have been used to determine the corresponding M-polynomials. Furthermore, by using the general form of M-polynomial of these nanostructures, some degree-based topological indices have been computed. In the end, the graphical representation of the M-polynomials is shown, and a detailed comparison between the obtained topological indices for aforementioned chemical structures is discussed.

Ensemble-Based Modeling of Chemical Compounds with Antimalarial Activity

2019 ◽  
Vol 19 (11) ◽  
pp. 957-969 ◽  
Author(s):  
Ana Yisel Caballero-Alfonso ◽  
Maykel Cruz-Monteagudo ◽  
Eduardo Tejera ◽  
Emilio Benfenati ◽  
Fernanda Borges ◽  
...  
Keyword(s):  
Genetic Algorithms ◽  
Computational Models ◽  
Antimalarial Activity ◽  
Majority Vote ◽  
Chemical Compounds ◽  
Quantitative Structure Activity Relationship ◽  
Qsar Modeling ◽  
Design Work ◽  
Data Set ◽  
Selection Of

Background: Malaria or Paludism is a tropical disease caused by parasites of the Plasmodium genre and transmitted to humans through the bite of infected mosquitos of the Anopheles genre. This pathology is considered one of the first causes of death in tropical countries and, despite several existing therapies, they have a high toxicity. Computational methods based on Quantitative Structure- Activity Relationship studies have been widely used in drug design work flows. Objective: The main goal of the current research is to develop computational models for the identification of antimalarial hit compounds. Materials and Methods: For this, a data set suitable for the modeling of the antimalarial activity of chemical compounds was compiled from the literature and subjected to a thorough curation process. In addition, the performance of a diverse set of ensemble-based classification methodologies was evaluated and one of these ensembles was selected as the most suitable for the identification of antimalarial hits based on its virtual screening performance. Data curation was conducted to minimize noise. Among the explored ensemble-based methods, the one combining Genetic Algorithms for the selection of the base classifiers and Majority Vote for their aggregation showed the best performance. Results: Our results also show that ensemble modeling is an effective strategy for the QSAR modeling of highly heterogeneous datasets in the discovery of potential antimalarial compounds. Conclusion: It was determined that the best performing ensembles were those that use Genetic Algorithms as a method of selection of base models and Majority Vote as the aggregation method.

scholarly journals A Mini Review on the Chemical Compounds of the Genus Acacia

2019 ◽  
Vol 22 (2) ◽  
pp. 235-242
Author(s):  
Mohammad Musarraf Hussain
Keyword(s):  
Chemical Compounds ◽  
Pharmaceutical Journal ◽  
Acacia Nilotica ◽  
Chemical Structures

The genus Acacia is a pioneering source of diversified chemical compounds. The purpose of this review is to compile of the phytochemicals from few species of Acacia. A total ten species of Acacia were studied and seventy six (1-76) phytoconstituents, including their chemical structures are reported in this review. The highest number of chemical compounds has been reported from Acacia nilotica. Bangladesh Pharmaceutical Journal 22(2): 235-242, 2019

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