Artificial Intelligence to Identify the Nectar Site: A Deep Learning Approach

2021 ◽  
Author(s):  
Dongpu Zhang ◽  
Suling Jia ◽  
Ji'An Zhao
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Approach

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

scholarly journals A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules

2021 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Mazen Ahmad ◽  
Ehecatl Antonio del Rio-Chanona ◽  
Jörg Kurt Wegner
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Molecular Target ◽  
Bioactive Molecules ◽  
Target Pair ◽  
Learning Approach ◽  
Scoring Functions ◽  
Structure Based Drug Design ◽  
Protein Targets ◽  
Better Than

Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. Herein, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. Concretely, the model learns a statistical potential based on distance likelihood which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands. We show that the potential based on distance likelihood described in this paper performs similar or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.

scholarly journals Artificial Intelligence Technique for Gene Expression by Tumor RNA-Seq Data: A Novel Optimized Deep Learning Approach

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 22874-22883 ◽  
Author(s):  
Nour Eldeen M. Khalifa ◽  
Mohamed Hamed N. Taha ◽  
Dalia Ezzat Ali ◽  
Adam Slowik ◽  
Aboul Ella Hassanien
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Approach ◽  
Rna Seq ◽  
Artificial Intelligence Technique ◽  
Intelligence Technique

scholarly journals A Geometric Deep Learning Approach to Predict Binding Conformations of Bioactive Molecules

2021 ◽  
Author(s):  
Oscar Méndez-Lucio ◽  
Mazen Ahmad ◽  
Ehecatl Antonio del Rio-Chanona ◽  
Jörg Kurt Wegner
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Molecular Target ◽  
Bioactive Molecules ◽  
Target Pair ◽  
Learning Approach ◽  
Scoring Functions ◽  
Structure Based Drug Design ◽  
Protein Targets ◽  
Better Than

Understanding the interactions formed between a ligand and its molecular target is key to guide the optimization of molecules. Different experimental and computational methods have been key to understand better these intermolecular interactions. Herein, we report a method based on geometric deep learning that is capable of predicting the binding conformations of ligands to protein targets. Concretely, the model learns a statistical potential based on distance likelihood which is tailor-made for each ligand-target pair. This potential can be coupled with global optimization algorithms to reproduce experimental binding conformations of ligands. We show that the potential based on distance likelihood described in this paper performs similar or better than well-established scoring functions for docking and screening tasks. Overall, this method represents an example of how artificial intelligence can be used to improve structure-based drug design.

scholarly journals A deep learning approach for detecting the behavior of people having personality disorders towards Covid-19 from Twitter

2021 ◽  
Author(s):  
Mourad Ellouze ◽  
Seifeddine Mechti ◽  
Moez Krichen ◽  
vinayakumar R ◽  
Lamia Hadrich Belguith
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Text Mining ◽  
Personality Disorders ◽  
Deep Neural Network ◽  
Selection Task ◽  
Learning Approach ◽  
Features Selection ◽  
Normal Behavior

This paper proposes an architecture taking advantage of artificial intelligence and text mining techniques in order to: (i) detect paranoid people by classifying their set of tweets into two classes (Paranoid/not-Paranoid), (ii) ensure the surveillance of these people by classifying their tweets about Covid-19 into two classes (person with normal behavior, person with inappropriate behavior). These objectives are achieved using an approach that takes advantage of different information related to the textual part, user and tweets for features selection task and deep neural network for the classification task. We obtained as an F-score rate 70% for the detection of paranoid people and 73% for the detection of the behavior of these people towards Covid-19. The obtained results are motivating and encouraging researchers to improve them given the interest and the importance of this research axis.

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