scholarly journals A Chemographic Audit of Anti-Coronavirus Structure-Activity Information from Public Databases (ChEMBL)

2020 ◽  
Author(s):  
Dragos Horvath ◽  
Alexey Orlov ◽  
Dmitry Osolodkin ◽  
Gilles Marcou ◽  
Aydar A. Ishmukhametov Ishmukhametov ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Chemical Space ◽  
Viral Proteins ◽  
Virus Species ◽  
Generative Topographic Mapping ◽  
Structure Activity ◽  
Definition Of ◽  
Pathogenic Agents ◽  
Activity Information ◽  
Selection Of

<p>Discovery of drugs against newly emerged pathogenic agents like the SARS-CoV-2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so-far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human-readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. </p> The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of “anti-CoV” map zones led to selection of therein residing 380 potential anti-CoV agents, out of a vast pool of 800M organic compounds.

scholarly journals A Chemographic Audit of Anti-Coronavirus Structure-Activity Information from Public Databases (ChEMBL)

2020 ◽  
Author(s):  
Dragos Horvath ◽  
Alexey Orlov ◽  
Dmitry Osolodkin ◽  
Gilles Marcou ◽  
Aydar A. Ishmukhametov Ishmukhametov ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Chemical Space ◽  
Viral Proteins ◽  
Virus Species ◽  
Generative Topographic Mapping ◽  
Structure Activity ◽  
Definition Of ◽  
Pathogenic Agents ◽  
Activity Information ◽  
Selection Of

<p>Discovery of drugs against newly emerged pathogenic agents like the SARS-CoV-2 coronavirus (CoV) must be based on previous research against related species. Scientists need to get acquainted with and develop a global oversight over so-far tested molecules. Chemography (herein used Generative Topographic Mapping, in particular) places structures on a human-readable 2D map (obtained by dimensionality reduction of the chemical space of molecular descriptors) and is thus well suited for such an audit. </p> The goal is to map medicinal chemistry efforts so far targeted against CoVs. This includes comparing libraries tested against various virus species/genera, predicting their polypharmacological profiles and highlighting often encountered chemotypes. Maps are challenged to provide predictive activity landscapes against viral proteins. Definition of “anti-CoV” map zones led to selection of therein residing 380 potential anti-CoV agents, out of a vast pool of 800M organic compounds.

scholarly journals Predicting the inhibition efficiencies of magnesium dissolution modulators using sparse machine learning models

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Elisabeth J. Schiessler ◽  
Tim Würger ◽  
Sviatlana V. Lamaka ◽  
Robert H. Meißner ◽  
Christian J. Cyron ◽  
...  
Keyword(s):  
Corrosion Inhibition ◽  
Molecular Descriptors ◽  
Chemical Space ◽  
Chemical Compounds ◽  
Automatic Identification ◽  
Organic Additives ◽  
Statistical Tool ◽  
Degradation Behaviour ◽  
Corrosion Inhibition Efficiency ◽  
Selection Of

AbstractThe degradation behaviour of magnesium and its alloys can be tuned by small organic molecules. However, an automatic identification of effective organic additives within the vast chemical space of potential compounds needs sophisticated tools. Herein, we propose two systematic approaches of sparse feature selection for identifying molecular descriptors that are most relevant for the corrosion inhibition efficiency of chemical compounds. One is based on the classical statistical tool of analysis of variance, the other one based on random forests. We demonstrate how both can—when combined with deep neural networks—help to predict the corrosion inhibition efficiencies of chemical compounds for the magnesium alloy ZE41. In particular, we demonstrate that this framework outperforms predictions relying on a random selection of molecular descriptors. Finally, we point out how autoencoders could be used in the future to enable even more accurate automated predictions of corrosion inhibition efficiencies.

Structure –Activity Relationships by Autocorrelation Descriptors and Genetic Algorithms

2011 ◽  
pp. 60-94 ◽  
Author(s):  
Viviana Consonni ◽  
Roberto Todeschini
Keyword(s):  
Molecular Structure ◽  
Molecular Descriptors ◽  
Biological Activities ◽  
Chemical Properties ◽  
Structural Features ◽  
Point Of View ◽  
Structure Activity Relationships ◽  
Structure Activity ◽  
Similarity Relationships ◽  
Definition Of

Quantitative Structure-Activity Relationships (QSARs) are models relating variation of molecule properties, such as biological activities, to variation of some structural features of chemical compounds. Three main topics take part of the QSAR/QSPR approach to the scientific research: the representation of molecular structure, the definition of molecular descriptors and the chemoinformatics tools. Molecular descriptors are numerical indices encoding some information related to the molecular structure. They can be both experimental physico-chemical properties of molecules and theoretical indices calculated by mathematical formulas or computational algorithms. In the last few decades, much interest has been addressed to studying how to encompass and convert the information encoded in the molecular structure into one or more numbers used to establish quantitative relationships between structures and properties, biological activities or other experimental properties. Autocorrelation descriptors are a class of molecular descriptors based on the statistical concept of spatial autocorrelation applied to the molecular structure. The objective of this chapter is to investigate the chemical information encompassed by autocorrelation descriptors and elucidate their role in QSAR and drug design. After a short introduction to molecular descriptors from a historical point of view, the chapter will focus on reviewing the different types of autocorrelation descriptors proposed in the literature so far. Then, some methodological topics related to multivariate data analysis will be overviewed paying particular attention to analysis of similarity/diversity of chemical spaces and feature selection for multiple linear regressions. The last part of the chapter will deal with application of autocorrelation descriptors to study similarity relationships of a set of flavonoids and establish QSARs for predicting affinity constants, Ki, to the GABAA benzodiazepine receptor site, BzR.

Selection and Preparation of Specific Tissue Regions For Tem Using Large Epoxy-Embedded Blocks

1973 ◽  
Vol 31 ◽  
pp. 324-325 ◽  
Author(s):  
P. M. Lowrie ◽  
W. S. Tyler
Keyword(s):  
Electron Microscopy ◽  
Anatomical Structures ◽  
Ultrastructural Studies ◽  
Transmission Electron ◽  
Microscopic Evaluation ◽  
Embedded Blocks ◽  
Specific Tissue ◽  
Definition Of ◽  
Areas Of Interest ◽  
Selection Of

The importance of examining stained 1 to 2μ plastic sections by light microscopy has long been recognized, both for increased definition of many histologic features and for selection of specimen samples to be used in ultrastructural studies. Selection of specimens with specific orien ation relative to anatomical structures becomes of critical importance in ultrastructural investigations of organs such as the lung. The uantity of blocks necessary to locate special areas of interest by random sampling is large, however, and the method is lacking in precision. Several methods have been described for selection of specific areas for electron microscopy using light microscopic evaluation of paraffin, epoxy-infiltrated, or epoxy-embedded large blocks from which thick sections were cut. Selected areas from these thick sections were subsequently removed and re-embedded or attached to blank precasted blocks and resectioned for transmission electron microscopy (TEM).

Rapid Elaboration of Fragments into Leads Applied to Bromodomain-3 Extra Terminal Domain

2020 ◽  
Author(s):  
Luke Adams ◽  
Lorna E. Wilkinson-White ◽  
Menachem J. Gunzburg ◽  
Stephen J. Headey ◽  
Martin J. Scanlon ◽  
...  
Keyword(s):  
Binding Site ◽  
Systematic Approach ◽  
Structural Information ◽  
Peptide Binding ◽  
Chemical Diversity ◽  
Chemical Probes ◽  
Protein Targets ◽  
Structure Activity ◽  
Peptide Binding Site ◽  
Selection Of

The development of low-affinity fragment hits into higher affinity leads is a major hurdle in fragment-based drug design. Here we demonstrate an approach for the Rapid Elaboration of Fragments into Leads (REFiL) applying an integrated workflow that provides a systematic approach to generate higher-affinity binders without the need for structural information. The workflow involves the selection of commercial analogues of fragment hits to generate preliminary structure-activity relationships. This is followed by parallel microscale chemistry using chemoinformatically designed reagent libraries to rapidly explore chemical diversity. Upon completion of a fragment screen against Bromodomain-3 extra terminal (BRD3-ET) domain we applied the REFiL workflow, which allowed us to develop a series of tetrahydrocarbazole ligands that bind to the peptide binding site of BRD3-ET. With REFiL we were able to rapidly improve binding affinity >30-fold. The REFiL workflow can be applied readily to a broad range of protein targets without the need of a structure, allowing the efficient evolution of low-affinity fragments into higher affinity leads and chemical probes.<br>

Applications of Quantitative Structure-Activity Relationships (QSAR) based Virtual Screening in Drug Design: A Review

2020 ◽  
Vol 20 (14) ◽  
pp. 1375-1388 ◽  
Author(s):  
Patnala Ganga Raju Achary
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Model Building ◽  
Chemical Space ◽  
Qsar Model ◽  
Quantitative Structure ◽  
Efficient Manner ◽  
Qsar Analysis ◽  
Structure Activity

The scientists, and the researchers around the globe generate tremendous amount of information everyday; for instance, so far more than 74 million molecules are registered in Chemical Abstract Services. According to a recent study, at present we have around 1060 molecules, which are classified as new drug-like molecules. The library of such molecules is now considered as ‘dark chemical space’ or ‘dark chemistry.’ Now, in order to explore such hidden molecules scientifically, a good number of live and updated databases (protein, cell, tissues, structure, drugs, etc.) are available today. The synchronization of the three different sciences: ‘genomics’, proteomics and ‘in-silico simulation’ will revolutionize the process of drug discovery. The screening of a sizable number of drugs like molecules is a challenge and it must be treated in an efficient manner. Virtual screening (VS) is an important computational tool in the drug discovery process; however, experimental verification of the drugs also equally important for the drug development process. The quantitative structure-activity relationship (QSAR) analysis is one of the machine learning technique, which is extensively used in VS techniques. QSAR is well-known for its high and fast throughput screening with a satisfactory hit rate. The QSAR model building involves (i) chemo-genomics data collection from a database or literature (ii) Calculation of right descriptors from molecular representation (iii) establishing a relationship (model) between biological activity and the selected descriptors (iv) application of QSAR model to predict the biological property for the molecules. All the hits obtained by the VS technique needs to be experimentally verified. The present mini-review highlights: the web-based machine learning tools, the role of QSAR in VS techniques, successful applications of QSAR based VS leading to the drug discovery and advantages and challenges of QSAR.

The quantitative structure-activity relationships between GABAA receptor and ligands based on binding interface characteristic

2020 ◽  
Vol 16 ◽  
Author(s):  
Shu Cheng ◽  
Yanrui Ding
Keyword(s):  
Gabaa Receptor ◽  
Molecular Descriptors ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Gradient Boosting ◽  
Receptor Interaction ◽  
Gamma Aminobutyric Acid ◽  
Quantitative Structure ◽  
Binding Interface ◽  
Structure Activity

Background: Quantitative Structure Activity Relationship (QSAR) methods based on machine learning play a vital role in predicting biological effect. Objective: Considering the characteristics of the binding interface between ligands and the inhibitory neurotransmitter Gamma Aminobutyric Acid A(GABAA) receptor, we built a QSAR model of ligands that bind to the human GABAA receptor. Method: After feature selection with Mean Decrease Impurity, we selected 53 from 1,286 docked ligand molecular descriptors. Three QSAR models are built using gradient boosting regression tree algorithm based on the different combinations of docked ligand molecular descriptors and ligand-receptor interaction characteristics. Results: The features of the optimal QSAR model contain both the docked ligand molecular descriptors and ligand-receptor interaction characteristics. The Leave-One-Out-Cross-Validation (Q2 LOO) of the optimal QSAR model is 0.8974, the Coefficient of Determination (R2) for the testing set is 0.9261, the Mean Square Error (MSE) is 0.1862. We also used this model to predict the pIC50 of two new ligands, the differences between the predicted and experimental pIC50 are -0.02 and 0.03 respectively. Conclusion : We found the BELm2, BELe2, MATS1m, X5v, Mor08v, and Mor29m are crucial features, which can help to build the QSAR model more accurately.

Studies on the EC50 of Natural Monoterpenes as Fungal Inhibitors with Quantitative Structure-Activity Relationships (QSARs)

2020 ◽  
Vol 10 (1) ◽  
pp. 44-60
Author(s):  
Mohamed E.I. Badawy ◽  
Entsar I. Rabea ◽  
Samir A.M. Abdelgaleil
Keyword(s):  
Biological Activity ◽  
Plant Pathogens ◽  
Molecular Descriptors ◽  
Microbial Pathogens ◽  
Quantitative Structure ◽  
Qsar Study ◽  
Qsar Analysis ◽  
Pharmacophore Modelling ◽  
Structure Activity ◽  
Wide Range

Background:Monoterpenes are the main constituents of the essential oils obtained from plants. These natural products offered wide spectra of biological activity and extensively tested against microbial pathogens and other agricultural pests.Methods:Antifungal activity of 10 monoterpenes, including two hydrocarbons (camphene and (S)- limonene) and eight oxygenated hydrocarbons ((R)-camphor, (R)-carvone, (S)-fenchone, geraniol, (R)-linalool, (+)-menthol, menthone, and thymol), was determined against fungi of Alternaria alternata, Botrytis cinerea, Botryodiplodia theobromae, Fusarium graminearum, Phoma exigua, Phytophthora infestans, and Sclerotinia sclerotiorum by the mycelia radial growth technique. Subsequently, Quantitative Structure-Activity Relationship (QSAR) analysis using different molecular descriptors with multiple regression analysis based on systematic search and LOOCV technique was performed. Moreover, pharmacophore modelling was carried out using LigandScout software to evaluate the common features essential for the activity and the hypothetical geometries adopted by these ligands in their most active forms.Results:The results showed that the antifungal activities were high, but depended on the chemical structure and the type of microorganism. Thymol showed the highest effect against all fungi tested with respective EC50 in the range of 10-86 mg/L. The QSAR study proved that the molecular descriptors HBA, MR, Pz, tPSA, and Vp were correlated positively with the biological activity in all of the best models with a correlation coefficient (r) ≥ 0.98 and cross-validated values (Q2) ≥ 0.77.Conclusion:The results of this work offer the opportunity to choose monoterpenes with preferential antimicrobial activity against a wide range of plant pathogens.

scholarly journals Promotion of Resilience in Migrants: A Systematic Review of Study and Psychosocial Intervention

2021 ◽  
Author(s):  
Maria Ciaramella ◽  
Nadia Monacelli ◽  
Livia Concetta Eugenia Cocimano
Keyword(s):  
Systematic Review ◽  
Psychosocial Intervention ◽  
Life Experiences ◽  
Psychosocial Interventions ◽  
Migrant Population ◽  
Methodological Choices ◽  
Research Questions ◽  
Definition Of ◽  
Selection Of

AbstractThis systematic review aimed to contribute to a better and more focused understanding of the link between the concept of resilience and psychosocial interventions in the migrant population. The research questions concerned the type of population involved, definition of resilience, methodological choices and which intervention programmes were targeted at migrants. In the 90 articles included, an heterogeneity in defining resilience or not well specified definition resulted. Different migratory experiences were not adequately considered in the selection of participants. Few resilience interventions on migrants were resulted. A lack of procedure’s descriptions that keep in account specific migrants’ life-experiences and efficacy’s measures were highlighted.

Sign in / Sign up

Export Citation Format

Share Document