Applications of Deep Learning for Drug Discovery Systems with BigData
The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, th...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | BioMedInformatics |
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| Online Access: | https://www.mdpi.com/2673-7426/2/4/39 |
| _version_ | 1797622127664300032 |
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| author | Yasunari Matsuzaka Ryu Yashiro |
| author_facet | Yasunari Matsuzaka Ryu Yashiro |
| author_sort | Yasunari Matsuzaka |
| collection | DOAJ |
| description | The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData. |
| first_indexed | 2024-03-11T09:05:56Z |
| format | Article |
| id | doaj.art-6467a3d824474885997ed7e9c0873d72 |
| institution | Directory Open Access Journal |
| issn | 2673-7426 |
| language | English |
| last_indexed | 2024-03-11T09:05:56Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj.art-6467a3d824474885997ed7e9c0873d722023-11-16T19:21:26ZengMDPI AGBioMedInformatics2673-74262022-11-012460362410.3390/biomedinformatics2040039Applications of Deep Learning for Drug Discovery Systems with BigDataYasunari Matsuzaka0Ryu Yashiro1Division of Molecular and Medical Genetics, Center for Gene and Cell Therapy, The Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, JapanAdministrative Section of Radiation Protection, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira 187-8551, Tokyo, JapanThe adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.https://www.mdpi.com/2673-7426/2/4/39artificial intelligence (AI)BigDataconvolutional neural networksdeep learningrecurrent neural network |
| spellingShingle | Yasunari Matsuzaka Ryu Yashiro Applications of Deep Learning for Drug Discovery Systems with BigData BioMedInformatics artificial intelligence (AI) BigData convolutional neural networks deep learning recurrent neural network |
| title | Applications of Deep Learning for Drug Discovery Systems with BigData |
| title_full | Applications of Deep Learning for Drug Discovery Systems with BigData |
| title_fullStr | Applications of Deep Learning for Drug Discovery Systems with BigData |
| title_full_unstemmed | Applications of Deep Learning for Drug Discovery Systems with BigData |
| title_short | Applications of Deep Learning for Drug Discovery Systems with BigData |
| title_sort | applications of deep learning for drug discovery systems with bigdata |
| topic | artificial intelligence (AI) BigData convolutional neural networks deep learning recurrent neural network |
| url | https://www.mdpi.com/2673-7426/2/4/39 |
| work_keys_str_mv | AT yasunarimatsuzaka applicationsofdeeplearningfordrugdiscoverysystemswithbigdata AT ryuyashiro applicationsofdeeplearningfordrugdiscoverysystemswithbigdata |