Development of a Neural Network-Based Approach for Prediction of Potential HIV-1 Entry Inhibitors Using Deep Learning and Molecular Modeling Methods

2020 ◽  
pp. 304-311
Author(s):  
Grigory I. Nikolaev ◽  
Nikita A. Shuldov ◽  
Arseny I. Anischenko ◽  
Alexander V. Tuzikov ◽  
Alexander M. Andrianov
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Molecular Modeling ◽  
Entry Inhibitors ◽  
Modeling Methods ◽  
Hiv 1

Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors

2021 ◽  
pp. 1-19
Author(s):  
Alexander M. Andrianov ◽  
Grigory I. Nikolaev ◽  
Nikita A. Shuldov ◽  
Ivan P. Bosko ◽  
Arseny I. Anischenko ◽  
...  
Keyword(s):  
Deep Learning ◽  
Molecular Modeling ◽  
Entry Inhibitors ◽  
Hiv 1

scholarly journals Design and Identification of Potential HIV-1 Entry Inhibitors Using In Silico Click Chemistry and Molecular Modeling Methods

2021 ◽  
Vol 16 (2) ◽  
pp. 317-334
Author(s):  
A.M. Andrianov ◽  
A.M. Yushkevich ◽  
I.P. Bosko ◽  
A.D. Karpenko ◽  
Yu.V. Kornoushenko ◽  
...  
Keyword(s):  
Molecular Modeling ◽  
Click Chemistry ◽  
Computer Aided Design ◽  
Integrated Approach ◽  
Viral Envelope ◽  
Entry Inhibitors ◽  
Fusion Inhibitors ◽  
Host Cell Membranes ◽  
Aided Design ◽  
Hiv 1

An integrated approach including the click chemistry methodology, molecular docking, quantum mechanics, and molecular dynamics was used to computer-aided design of potential HIV-1 inhibitors able to block the membrane-proximal external region (MPER) of HIV-1 gp41, which plays an important role in the fusion of the viral and host cell membranes. Evaluation of the binding efficiency of the designed compounds to the HIV-1 MPER peptide was performed using the methods of molecular modeling, resulting in nine chemical compounds exhibiting high-affinity binding to this functionally important site of the trimeric “spike” of the viral envelope. The data obtained indicate that the identified compounds are promising for the development of novel antiviral drugs, HIV fusion inhibitors blocking the early stages of HIV infection.

scholarly journals Development of a generative adversarial neural network for identification of potential HIV-1 inhibitors by deep learning methods

Informatics ◽  
2020 ◽  
Vol 17 (1) ◽  
pp. 7-17
Author(s):  
G. I. Nikolaev ◽  
N. A. Shuldov ◽  
A. I. Anishenko, ◽  
A. V. Tuzikov ◽  
A. M. Andrianov
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Rational Design ◽  
Binding Free Energy ◽  
Viral Envelope ◽  
Training Dataset ◽  
Learning Methods ◽  
The Neural Network ◽  
Wide Range ◽  
Hiv 1

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block the region of the viral envelope protein gp120 critical for the virus binding to cellular receptor CD4 was developed using deep learning methods. The research were carried out to create the  architecture of the neural network, to form  virtual compound library of potential anti-HIV-1 agents for training the neural network, to make  molecular docking of all compounds from this library with gp120, to  calculate the values of binding free energy, to generate molecular fingerprints for chemical compounds from the training dataset. The training the neural network was implemented followed by estimation of the learning outcomes and work of the autoencoder.  The validation of the neural network on a wide range of compounds from the ZINC database was carried out. The use of the neural network in combination with virtual screening of chemical databases was shown to form a productive platform for identifying the basic structures promising for the design of novel antiviral drugs that inhibit the early stages of HIV infection.

Insight into the Inhibitory Mechanism and Binding Mode Between D77 and HIV-1 Integrase by Molecular Modeling Methods

2011 ◽  
Vol 29 (2) ◽  
pp. 311-323 ◽  
Author(s):  
Ping Li ◽  
Jian Jun Tan ◽  
Ming Liu ◽  
Xiao Yi Zhang ◽  
Wei Zu Chen ◽  
...  
Keyword(s):  
Molecular Modeling ◽  
Binding Mode ◽  
Inhibitory Mechanism ◽  
Modeling Methods ◽  
Insight Into ◽  
Hiv 1

scholarly journals Drug Resistance Prediction Using Deep Learning Techniques on HIV-1 Sequence Data

Viruses ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 560
Author(s):  
Margaret C. Steiner ◽  
Keylie M. Gibson ◽  
Keith A. Crandall
Keyword(s):  
Neural Network ◽  
Drug Resistance ◽  
Deep Learning ◽  
Sequence Data ◽  
Classification Performance ◽  
Resistance Mutations ◽  
Evolutionary Mechanisms ◽  
Drug Resistance Prediction ◽  
Resistance Prediction ◽  
Hiv 1

The fast replication rate and lack of repair mechanisms of human immunodeficiency virus (HIV) contribute to its high mutation frequency, with some mutations resulting in the evolution of resistance to antiretroviral therapies (ART). As such, studying HIV drug resistance allows for real-time evaluation of evolutionary mechanisms. Characterizing the biological process of drug resistance is also critically important for sustained effectiveness of ART. Investigating the link between “black box” deep learning methods applied to this problem and evolutionary principles governing drug resistance has been overlooked to date. Here, we utilized publicly available HIV-1 sequence data and drug resistance assay results for 18 ART drugs to evaluate the performance of three architectures (multilayer perceptron, bidirectional recurrent neural network, and convolutional neural network) for drug resistance prediction, jointly with biological analysis. We identified convolutional neural networks as the best performing architecture and displayed a correspondence between the importance of biologically relevant features in the classifier and overall performance. Our results suggest that the high classification performance of deep learning models is indeed dependent on drug resistance mutations (DRMs). These models heavily weighted several features that are not known DRM locations, indicating the utility of model interpretability to address causal relationships in viral genotype-phenotype data.

scholarly journals Application of Deep Learning and Molecular Modelling Methods to Identify Potential HIV-1 Entry Inhibitors

2020 ◽  
Author(s):  
G.I. Nikolaev ◽  
N.A. Shuldov ◽  
I.P. Bosko ◽  
A.I. Anischenko ◽  
A.V. Tuzikov ◽  
...  
Keyword(s):  
Deep Learning ◽  
Molecular Modelling ◽  
Entry Inhibitors ◽  
Modelling Methods ◽  
Hiv 1

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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