scholarly journals A Benchmark Dataset for Computational Drug Repositioning

2015 ◽  
Vol 1 (2) ◽  
pp. 10-12 ◽  
Author(s):  
Maria Kissa ◽  
George Tsatsaronis
Keyword(s):  
Drug Repositioning ◽  
Benchmark Dataset

Improving the Accuracy of Protein-Ligand Binding Affinity Prediction by Deep Learning Models: Benchmark and Model

2019 ◽  
Author(s):  
Mohammad Rezaei ◽  
Yanjun Li ◽  
Xiaolin Li ◽  
Chenglong Li
Keyword(s):  
Deep Learning ◽  
Drug Design ◽  
Binding Affinity ◽  
Benchmark Dataset ◽  
Rational Drug Design ◽  
Learning Models ◽  
Structure Based Drug Design ◽  
Binding Affinity Prediction ◽  
Affinity Prediction ◽  
Rational Drug

<b>Introduction:</b> The ability to discriminate among ligands binding to the same protein target in terms of their relative binding affinity lies at the heart of structure-based drug design. Any improvement in the accuracy and reliability of binding affinity prediction methods decreases the discrepancy between experimental and computational results.<br><b>Objectives:</b> The primary objectives were to find the most relevant features affecting binding affinity prediction, least use of manual feature engineering, and improving the reliability of binding affinity prediction using efficient deep learning models by tuning the model hyperparameters.<br><b>Methods:</b> The binding site of target proteins was represented as a grid box around their bound ligand. Both binary and distance-dependent occupancies were examined for how an atom affects its neighbor voxels in this grid. A combination of different features including ANOLEA, ligand elements, and Arpeggio atom types were used to represent the input. An efficient convolutional neural network (CNN) architecture, DeepAtom, was developed, trained and tested on the PDBbind v2016 dataset. Additionally an extended benchmark dataset was compiled to train and evaluate the models.<br><b>Results: </b>The best DeepAtom model showed an improved accuracy in the binding affinity prediction on PDBbind core subset (Pearson’s R=0.83) and is better than the recent state-of-the-art models in this field. In addition when the DeepAtom model was trained on our proposed benchmark dataset, it yields higher correlation compared to the baseline which confirms the value of our model.<br><b>Conclusions:</b> The promising results for the predicted binding affinities is expected to pave the way for embedding deep learning models in virtual screening and rational drug design fields.

Computational Approaches to Analyze the Strategies of Drug Repositioning*

2012 ◽  
Vol 39 (11) ◽  
pp. 1029-1036
Author(s):  
Da-Fei XIE ◽  
Peng LI ◽  
Fei LI ◽  
Xiao-Chen BO ◽  
Sheng-Qi WANG
Keyword(s):  
Drug Repositioning ◽  
Computational Approaches

Repositioning Drugs for Rare Immune Diseases: Hopes and Challenges for a Precision Medicine

2018 ◽  
Vol 25 (24) ◽  
pp. 2764-2782 ◽  
Author(s):  
Erica Valencic ◽  
Alenka Smid ◽  
Ziga Jakopin ◽  
Alberto Tommasini ◽  
Irena Mlinaric-Rascan
Keyword(s):  
Precision Medicine ◽  
Rare Diseases ◽  
Business Models ◽  
Drug Repositioning ◽  
Drug Action ◽  
Primary Immunodeficiency Diseases ◽  
Computational Sciences ◽  
Approved Drugs ◽  
Molecular Mode ◽  
Viable Business

Human primary immunodeficiency diseases (PIDs) are a large group of rare diseases and are characterized by a great genetic and phenotypic heterogeneity. A large subset of PIDs is genetically defined, which has a crucial impact for the understanding of the molecular basis of disease and the development of precision medicine. <p> Discovery and development of new therapies for rare diseases has long been de-privileged due to the length and cost of the processes involved. Interest has increased due to stimulatory regulatory and supportive reimbursement environments enabling viable business models. <p> Advancements in biomedical and computational sciences enable the development of rational, designed approaches for identification of novel indications of already approved drugs allowing faster delivery of new medicines. Drug repositioning is based either on clinical analogies of diseases or on understanding of the molecular mode of drug action and mechanisms of the disease. All of these are the basis for the development of precision medicine.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Towards Better Drug Repositioning: Targeted Immunoinflammatory Therapy for Diabetic Nephropathy

2019 ◽  
Vol 26 ◽  
Author(s):  
Qin zhang ◽  
Ming Yang ◽  
Ying Xiao ◽  
Yachun Han ◽  
Shikun Yang ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Diabetic Nephropathy ◽  
Inflammatory Process ◽  
Drug Repositioning ◽  
Microvascular Complications ◽  
Pathogenic Factor ◽  
Progressive Decline ◽  
Functional Changes ◽  
Sequence Of Events ◽  
Novel Treatment

: Diabetic nephropathy (DN) is one of the most common and important microvascular complications of diabetes mellitus (DM). The main clinical features of DN are proteinuria and a progressive decline in renal function , which are associated with structural and functional changes in the kidney. The pathogenesis of DN is multifactorial, including genetic, metabolic and haemodynamic factors, which can trigger a sequence of events. Controlling metabolic risks such as hyperglycaemia, hypertension and dyslipidaemia is not enough to slow the progression of DN. Recent studies have emphasized immunoinflammation as a critical pathogenic factor in the progression of DN. Therefore, targeting inflammation is considered a potential and novel treatment strategy for DN. In this review, we will briefly introduce the inflammatory process of DN and discuss the anti-inflammatory effects of antidiabetic drugs when treating DN.

Computational Drug Repositioning by Target Hopping: A Use Case in Chagas Disease

2016 ◽  
Vol 22 (21) ◽  
pp. 3124-3134 ◽  
Author(s):  
V. Joachim Haupt ◽  
Jesús E. Aguilar Uvalle ◽  
Sebastian Salentin ◽  
Simone Daminelli ◽  
Franziska Leonhardt ◽  
...  
Keyword(s):  
Chagas Disease ◽  
Drug Repositioning ◽  
Use Case

Addressing the importance of Stem cell-based therapy: a Perspective in the treatment of COVID-19’

2020 ◽  
Vol 20 ◽  
Author(s):  
Christine Ibrahim ◽  
Hanna Semaan ◽  
Marwan El-Sabban ◽  
Fadia Najjar ◽  
Aline Hamade
Keyword(s):  
Stem Cell ◽  
Drug Repositioning ◽  
World Health ◽  
Unknown Etiology ◽  
Cell Based Therapy ◽  
Pathogenic Virus ◽  
Lung Injuries ◽  
Healthcare Crisis ◽  
Health Organization ◽  
Potential Use

: Severe acute respiratory syndrome-associated corona virus 2 (SARS-CoV-2), is an extremely pathogenic virus belonging to the family of Coronaviridae. First identified in Wuhan China in December 2019 after an epidemiological investigation of an emerging cluster of pneumonia of unknown etiology, SARS-CoV-2 was declared the cause of a pandemic on March 11 by the World Health Organization (WHO) pointing to the over 118000 cases of Coronavirus disease 2019 (COVID- 19) in over 110 countries. Despite the promising results of drug repositioning studies in the treatment of COVID-19, the evidence of their safety and efficacy remains inconclusive. Cell based therapy has been proven safe and possibly effective in treating multiple lung injuries and diseases but its potential use in the treatment of COVID-19 has not been yet elucidated. Our aim in this review is to provide an overview on the immunomodulatory effect and the regenerative capacity of stem cells and their secretome in the treatment of many diseases including lung injuries. Those findings may contribute to a better understanding of the potential of stem cell therapy in SARS-CoV-2 infection and its potential use in order to find a solution for this healthcare crisis.

Molecular Topology and Other Promiscuity Determinants as Predictors of Therapeutic Class - A Theoretical Framework to Guide Drug Repositioning?

2018 ◽  
Vol 18 (13) ◽  
pp. 1110-1122 ◽  
Author(s):  
Juan F. Morales ◽  
Lucas N. Alberca ◽  
Sara Chuguransky ◽  
Mauricio E. Di Ianni ◽  
Alan Talevi ◽  
...  
Keyword(s):  
Molecular Descriptors ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Topological Descriptors ◽  
Log P ◽  
Acidity Constant ◽  
Molecular Topology ◽  
Clustering Methods ◽  
Mean Values ◽  
Qsar Models

Much interest has been paid in the last decade on molecular predictors of promiscuity, including molecular weight, log P, molecular complexity, acidity constant and molecular topology, with correlations between promiscuity and those descriptors seemingly being context-dependent. It has been observed that certain therapeutic categories (e.g. mood disorders therapies) display a tendency to include multi-target agents (i.e. selective non-selectivity). Numerous QSAR models based on topological descriptors suggest that the topology of a given drug could be used to infer its therapeutic applications. Here, we have used descriptive statistics to explore the distribution of molecular topology descriptors and other promiscuity predictors across different therapeutic categories. Working with the publicly available ChEMBL database and 14 molecular descriptors, both hierarchical and non-hierchical clustering methods were applied to the descriptors mean values of the therapeutic categories after the refinement of the database (770 drugs grouped into 34 therapeutic categories). On the other hand, another publicly available database (repoDB) was used to retrieve cases of clinically-approved drug repositioning examples that could be classified into the therapeutic categories considered by the aforementioned clusters (111 cases), and the correspondence between the two studies was evaluated. Interestingly, a 3- cluster hierarchical clustering scheme based on only 14 molecular descriptors linked to promiscuity seem to explain up to 82.9% of approved cases of drug repurposing retrieved of repoDB. Therapeutic categories seem to display distinctive molecular patterns, which could be used as a basis for drug screening and drug design campaigns, and to unveil drug repurposing opportunities between particular therapeutic categories.

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