scholarly journals Turning a Drug Target into a Drug Candidate: A New Paradigm for Neurological Drug Discovery?

BioEssays ◽  
2020 ◽  
Vol 42 (9) ◽  
pp. 2000011
Author(s):  
Steven D. Buckingham ◽  
Harry‐Jack Mann ◽  
Olivia K. Hearnden ◽  
David B. Sattelle
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Drug Candidate ◽  
New Paradigm

scholarly journals DEEPScreen: High Performance Drug-Target Interaction Prediction with Convolutional Neural Networks Using 2-D Structural Compound Representations

2018 ◽  
Author(s):  
Ahmet Sureyya Rifaioglu ◽  
Volkan Atalay ◽  
Maria Jesus Martin ◽  
Rengul Cetin-Atalay ◽  
Tunca Dogan
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Drug Discovery ◽  
Drug Target ◽  
Large Scale ◽  
Experimental Studies ◽  
Drug Candidate ◽  
Support Vector ◽  
Target Proteins ◽  
Interaction Prediction

The identification of physical interactions between drug candidate chemical substances and target biomolecules is an important step in the process of drug discovery, where the standard procedure is the systematic screening of chemical compounds against pre-selected target proteins. However, experimental screening procedures are expensive and time consuming, therefore, it is not possible to carry out comprehensive tests. Within the last decade, computational approaches have been developed with the objective of aiding experimental studies by predicting novel drug-target interactions (DTI), via the construction and application of statistical models. In this study, we propose a large-scale DTI interaction prediction system, DEEPScreen, for early stage drug discovery, using convolutional deep neural networks. One of the main advantages of DEEPScreen is employing readily available simple 2-D images of compounds at the input level instead of engineered complex feature vectors that displayed limited performance in DTI prediction tasks previously. DEEPScreen learns complex features inherently from the 2-D molecular representations, thus producing highly accurate predictions. DEEPScreen system was trained for 704 target proteins (using ChEMBL curated bioactivity data) and finalized with rigorous hyper-parameter optimization tests. We compared the performance of DEEPScreen against shallow classifiers such as the random forest, logistic regression and support vector machines, to indicate the effectiveness of the proposed deep learning approach. Additionally, we compared DEEPScreen with other deep learning based state-of-the-art DTI predictors on widely used benchmark datasets and showed that DEEPScreen produces better or comparable results to the top performers. The method proposed here can be employed to computationally scan a large portion of the recorded drug candidate compound and protein spaces to aid the experimentalists working in the field of drug discovery and repurposing by providing a preselection of interesting novel DTIs.

scholarly journals Large scale enterohemorrhagic E coli population genomic analysis using whole genome typing reveals recombination clusters and potential drug target

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 33
Author(s):  
DJ Darwin Bandoy
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Large Scale ◽  
Genomic Analysis ◽  
Wide Distribution ◽  
Whole Genome ◽  
Potential Drug Target ◽  
Potential Drug ◽  
New Paradigm ◽  
Population Genomic

Enterohemorrhagic Escherichia coli continues to be a significant public health risk. With the onset of next generation sequencing, whole genome sequences require a new paradigm of analysis relevant for epidemiology and drug discovery. A large-scale bacterial population genomic analysis was applied to 702 isolates of serotypes associated with EHEC resulting in five pangenome clusters. Serotype incongruence with pangenome types suggests recombination clusters. Core genome analysis was performed to determine the population wide distribution of sdiA as potential drug target. Protein modelling revealed nonsynonymous variants are notably absent in the ligand binding site for quorum sensing, indicating that population wide conservation of the sdiA ligand site can be targeted for potential prophylactic purposes. Applying pathotype-wide pangenomics as a guide for determining evolution of pharmacophore sites is a potential approach in drug discovery.

scholarly journals Large scale enterohemorrhagic E coli population genomic analysis using whole genome typing reveals recombination clusters and potential drug target

F1000Research ◽  
2020 ◽  
Vol 8 ◽  
pp. 33
Author(s):  
DJ Darwin Bandoy
Keyword(s):  
Drug Discovery ◽  
Drug Target ◽  
Large Scale ◽  
Genomic Analysis ◽  
Wide Distribution ◽  
Whole Genome ◽  
Potential Drug Target ◽  
Potential Drug ◽  
New Paradigm ◽  
Population Genomic

Enterohemorrhagic Escherichia coli continues to be a significant public health risk. With the onset of next generation sequencing, whole genome sequences require a new paradigm of analysis relevant for epidemiology and drug discovery. A large-scale bacterial population genomic analysis was applied to 702 isolates of serotypes associated with EHEC resulting in five pangenome clusters. Serotype incongruence with pangenome types suggests recombination clusters. Core genome analysis was performed to determine the population wide distribution of sdiA as potential drug target. Protein modelling revealed nonsynonymous variants are notably absent in the ligand binding site for quorum sensing, indicating that population wide conservation of the sdiA ligand site can be targeted for potential prophylactic purposes. Applying pathotype-wide pangenomics as a guide for determining evolution of pharmacophore sites is a potential approach in drug discovery.

Use of Molecular Dynamics Simulations in Structure-Based Drug Discovery

2019 ◽  
Vol 25 (31) ◽  
pp. 3339-3349 ◽  
Author(s):  
Indrani Bera ◽  
Pavan V. Payghan
Keyword(s):  
Molecular Dynamics ◽  
Drug Discovery ◽  
Molecular Dynamics Simulations ◽  
Drug Target ◽  
Structural Information ◽  
Drug Binding ◽  
Structure Based Drug Design ◽  
Drug Designing ◽  
Target Binding ◽  
Dynamics Simulations

Background: Traditional drug discovery is a lengthy process which involves a huge amount of resources. Modern-day drug discovers various multidisciplinary approaches amongst which, computational ligand and structure-based drug designing methods contribute significantly. Structure-based drug designing techniques require the knowledge of structural information of drug target and drug-target complexes. Proper understanding of drug-target binding requires the flexibility of both ligand and receptor to be incorporated. Molecular docking refers to the static picture of the drug-target complex(es). Molecular dynamics, on the other hand, introduces flexibility to understand the drug binding process. Objective: The aim of the present study is to provide a systematic review on the usage of molecular dynamics simulations to aid the process of structure-based drug design. Method: This review discussed findings from various research articles and review papers on the use of molecular dynamics in drug discovery. All efforts highlight the practical grounds for which molecular dynamics simulations are used in drug designing program. In summary, various aspects of the use of molecular dynamics simulations that underline the basis of studying drug-target complexes were thoroughly explained. Results: This review is the result of reviewing more than a hundred papers. It summarizes various problems that use molecular dynamics simulations. Conclusion: The findings of this review highlight how molecular dynamics simulations have been successfully implemented to study the structure-function details of specific drug-target complexes. It also identifies the key areas such as stability of drug-target complexes, ligand binding kinetics and identification of allosteric sites which have been elucidated using molecular dynamics simulations.

scholarly journals N-Acetylglucosamine-1-Phosphate Transferase, WecA, as a Validated Drug Target in Mycobacterium tuberculosis

2017 ◽  
Vol 61 (11) ◽  
Author(s):  
Stanislav Huszár ◽  
Vinayak Singh ◽  
Alica Polčicová ◽  
Peter Baráth ◽  
María Belén Barrio ◽  
...  
Keyword(s):  
Mycobacterium Tuberculosis ◽  
Drug Target ◽  
Functional Characterization ◽  
Drug Candidate ◽  
Content Type ◽  
Chemical Libraries ◽  
Whole Cells ◽  
Encoding Gene

ABSTRACT The mycobacterial phosphoglycosyltransferase WecA, which initiates arabinogalactan biosynthesis in Mycobacterium tuberculosis, has been proposed as a target of the caprazamycin derivative CPZEN-45, a preclinical drug candidate for the treatment of tuberculosis. In this report, we describe the functional characterization of mycobacterial WecA and confirm the essentiality of its encoding gene in M. tuberculosis by demonstrating that the transcriptional silencing of wecA is bactericidal in vitro and in macrophages. Silencing wecA also conferred hypersensitivity of M. tuberculosis to the drug tunicamycin, confirming its target selectivity for WecA in whole cells. Simple radiometric assays performed with mycobacterial membranes and commercially available substrates allowed chemical validation of other putative WecA inhibitors and resolved their selectivity toward WecA versus another attractive cell wall target, translocase I, which catalyzes the first membrane step in the biosynthesis of peptidoglycan. These assays and the mutant strain described herein will be useful for identifying potential antitubercular leads by screening chemical libraries for novel WecA inhibitors.

Synthesis of Pyrazolofuropyrazine via One-Pot SNAr Reaction and Intramolecular Direct C–H Arylation

Synthesis ◽  
2018 ◽  
Vol 50 (07) ◽  
pp. 1493-1498 ◽  
Author(s):  
Shinichiro Fuse ◽  
Hiroyuki Nakamura ◽  
Megumi Inaba ◽  
Shinichi Sato ◽  
Manjusha Joshi
Keyword(s):  
Drug Discovery ◽  
Rapid Development ◽  
Ring System ◽  
Biologically Active ◽  
Drug Candidate ◽  
One Pot ◽  
Ring Systems ◽  
Drug Candidates ◽  
Fused Ring ◽  
Snar Reaction

Fused-ring systems containing heterocycles are attractive templates for drug discovery. Biologically active 6-5-5+6 fused-ring systems that possess heterocycles are available, but these require a relatively large number of synthetic steps for preparation. Therefore, pyrazolofuropyrazine was designed as a 6-5-5+6 ring system template that incorporates ready accessibility for drug discovery. Pyrazolofuropyrazines were successfully constructed in only a few steps via one-pot SNAr reaction/intramolecular C–H direct arylation. As a drug candidate, pyrazolofuropyrazine has earned a favorable LogP, although significant biological activity has yet to be established; the ready accessibility of pyrazolofuropyrazine template, however, offers an opportunity for the rapid development of promising new drug candidates.

scholarly journals Molecular Hybridization as a Tool in the Design of Multi-target Directed Drug Candidates for Neurodegenerative Diseases

2020 ◽  
Vol 18 (5) ◽  
pp. 348-407 ◽  
Author(s):  
Vanessa Silva Gontijo ◽  
Flávia P. Dias Viegas ◽  
Cindy Juliet Cristancho Ortiz ◽  
Matheus de Freitas Silva ◽  
Caio Miranda Damasio ◽  
...  
Keyword(s):  
Neurodegenerative Diseases ◽  
Rational Design ◽  
Brain Regions ◽  
Molecular Hybridization ◽  
Drug Candidate ◽  
New Paradigm ◽  
New Approach ◽  
Biological Targets ◽  
Drug Candidates ◽  
Biochemical Pathways

Neurodegenerative Diseases (NDs) are progressive multifactorial neurological pathologies related to neuronal impairment and functional loss from different brain regions. Currently, no effective treatments are available for any NDs, and this lack of efficacy has been attributed to the multitude of interconnected factors involved in their pathophysiology. In the last two decades, a new approach for the rational design of new drug candidates, also called multitarget-directed ligands (MTDLs) strategy, has emerged and has been used in the design and for the development of a variety of hybrid compounds capable to act simultaneously in diverse biological targets. Based on the polypharmacology concept, this new paradigm has been thought as a more secure and effective way for modulating concomitantly two or more biochemical pathways responsible for the onset and progress of NDs, trying to overcome low therapeutical effectiveness. As a complement to our previous review article (Curr. Med. Chem. 2007, 14 (17), 1829-1852. https://doi.org/10.2174/092986707781058805), herein we aimed to cover the period from 2008 to 2019 and highlight the most recent advances of the exploitation of Molecular Hybridization (MH) as a tool in the rational design of innovative multifunctional drug candidate prototypes for the treatment of NDs, specially focused on AD, PD, HD and ALS.

scholarly journals Multivariate Classification of Drugs using Parametric and Nonparametric Machine Learning Models

2020 ◽  
Vol 9 (3) ◽  
pp. 2021-2027
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Biological Activities ◽  
Biological Effects ◽  
Recursive Feature Elimination ◽  
Drug Candidate ◽  
Learning Models ◽  
Machine Learning Models ◽  
Non Parametric

In pharmaceutical research, traditional drug discovery process is time consuming and expensive, where several compounds are experimentally tested for their biological activities. Series of lab experiments are conducted to analyze newly synthesized drug’s pharmaceutical activities and its biological effects on human. With every new drug discovery, the required clinical properties can be determined using machine learning models and this greatly reduces the experimental cost. This paper explores parametric and non-parametric machine learning models to classify administration properties of drugs and its toxicity. The multinomial classification of drugs was based on their physicochemical and ADMET properties. Balanced data samples were drawn from chEMBL and was pre-processed. Features were reduced using Recursive Feature Elimination and the attributes were ranked based on their importance to reduce highly correlated attributes. The performance of parametric and non-parametric machine learning models was analyzed on cheminformatic data that includes physiochemical, biological and pharmaceutical properties of the drug molecules. Selecting the potent drug candidate along with its administration properties greatly reduces wet lab experimental time and cost. Multiclass classification can be determined efficiently using non-parametric machine learning model. Optimal feature engineering, tuning hyperparameters and adopting hybrid algorithms would result in more accurate predictions in future for cheminformatics data.

scholarly journals Open-source chemogenomic data-driven algorithms for predicting drug–target interactions

2018 ◽  
Vol 20 (4) ◽  
pp. 1465-1474 ◽  
Author(s):  
Ming Hao ◽  
Stephen H Bryant ◽  
Yanli Wang
Keyword(s):  
Drug Discovery ◽  
Human Genome ◽  
High Throughput Screening ◽  
Drug Target ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Research Field ◽  
Data Driven ◽  
Source Codes ◽  
Novel Technologies

AbstractWhile novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug–target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred.

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