Quantitative neurotoxicology: Potential role of artificial intelligence/deep learning approach

2020 ◽  
Author(s):  
Anshul Srivastava ◽  
Joseph P. Hanig
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Potential Role ◽  
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 Emerging role of deep learning‐based artificial intelligence in tumor pathology

2020 ◽  
Vol 40 (4) ◽  
pp. 154-166 ◽  
Author(s):  
Yahui Jiang ◽  
Meng Yang ◽  
Shuhao Wang ◽  
Xiangchun Li ◽  
Yan Sun
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Tumor Pathology
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