Artificial intelligence and big data facilitated targeted drug discovery

Different kinds of biological databases publicly available nowadays provide us a goldmine of multidiscipline big data. The Cancer Genome Atlas is a cancer database including detailed information of many patients with cancer. DrugBank is a database including detailed information of approved, investigational and withdrawn drugs, as well as other nutraceutical and metabolite structures. PubChem is a chemical compound database including all commercially available compounds as well as other synthesisable compounds. Protein Data Bank is a crystal structure database including X-ray, cryo-EM and nuclear magnetic resonance protein three-dimensional structures as well as their ligands. On the other hand, artificial intelligence (AI) is playing an important role in the drug discovery progress. The integration of such big data and AI is making a great difference in the discovery of novel targeted drug. In this review, we focus on the currently available advanced methods for the discovery of highly effective lead compounds with great absorption, distribution, metabolism, excretion and toxicity properties.

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Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery

Molecular Diversity ◽

10.1007/s11030-021-10256-w ◽

2021 ◽

Author(s):

Manish Kumar Tripathi ◽

Abhigyan Nath ◽

Tej P. Singh ◽

A. S. Ethayathulla ◽

Punit Kaur

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Drug Discovery

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Research on the university intelligent learning analysis system based on AI

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189820 ◽

2021 ◽

pp. 1-10

Author(s):

Meng Huang ◽

Shuai Liu ◽

Yahao Zhang ◽

Kewei Cui ◽

Yana Wen

Keyword(s):

Artificial Intelligence ◽

Big Data ◽

Academic Performance ◽

Clustering Algorithm ◽

Back Propagation ◽

Three Dimensional ◽

Training Model ◽

Future Trend ◽

Artificial Intelligence Technology ◽

Visualization Technology

The integration of Artificial Intelligence technology and school education had become a future trend, and became an important driving force for the development of education. With the advent of the era of big data, although the relationship between students’ learning status data was closer to nonlinear relationship, combined with the application analysis of artificial intelligence technology, it could be found that students’ living habits were closely related to their academic performance. In this paper, through the investigation and analysis of the living habits and learning conditions of more than 2000 students in the past 10 grades in Information College of Institute of Disaster Prevention, we used the hierarchical clustering algorithm to classify the nearly 180000 records collected, and used the big data visualization technology of Echarts + iView + GIS and the JavaScript development method to dynamically display the students’ life track and learning information based on the map, then apply Three Dimensional ArcGIS for JS API technology showed the network infrastructure of the campus. Finally, a training model was established based on the historical learning achievements, life trajectory, graduates’ salary, school infrastructure and other information combined with the artificial intelligence Back Propagation neural network algorithm. Through the analysis of the training resulted, it was found that the students’ academic performance was related to the reasonable laboratory study time, dormitory stay time, physical exercise time and social entertainment time. Finally, the system could intelligently predict students’ academic performance and give reasonable suggestions according to the established prediction model. The realization of this project could provide technical support for university educators.

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