Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery
| Autor: | Abdul S. Ethayathulla, Punit Kaur, Tej P. Singh, Abhigyan Nath, Manish Kumar Tripathi |
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| Rok vydání: | 2021 |
| Předmět: |
Big Data
Artificial intelligence Databases Factual Computer science Big data Autoencoders 010402 general chemistry 01 natural sciences Catalysis Workflow Machine Learning Inorganic Chemistry Drug Discovery Physical and Theoretical Chemistry Molecular Biology Artificial neural network 010405 organic chemistry business.industry Drug discovery Deep learning Organic Chemistry Reproducibility of Results Chemical data General Medicine Chemical space 0104 chemical sciences ComputingMethodologies_PATTERNRECOGNITION Cheminformatics Drug Design Paradigm shift Original Article business Algorithms Information Systems |
| Zdroj: | Molecular Diversity |
| ISSN: | 1573-501X 1381-1991 |
| DOI: | 10.1007/s11030-021-10256-w |
| Popis: | The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines. Graphic abstract |
| Databáze: | OpenAIRE |
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