Data considerations for predictive modeling applied to the discovery of bioactive natural products
Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the large amount of generated data is complex and difficul...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/161541 |
| _version_ | 1826120438571335680 |
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| author | Xue, Hai Tao Stanley-Baker, Michael Kong, Adams Wai Kin Li, Hoi Leung Goh, Wilson Wen Bin |
| author2 | School of Biological Sciences |
| author_facet | School of Biological Sciences Xue, Hai Tao Stanley-Baker, Michael Kong, Adams Wai Kin Li, Hoi Leung Goh, Wilson Wen Bin |
| author_sort | Xue, Hai Tao |
| collection | NTU |
| description | Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the large amount of generated data is complex and difficult to analyze effectively. This limitation is increasingly surmounted by artificial intelligence (AI) techniques but more needs to be done. Here, we present and discuss two crucial issues limiting NP-AI drug discovery: the first is on knowledge and resource development (data integration) to bridge the gap between NPs and functional or therapeutic effects. The second issue is on NP-AI modeling considerations, limitations and challenges. |
| first_indexed | 2024-10-01T05:16:45Z |
| format | Journal Article |
| id | ntu-10356/161541 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:16:45Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-10356/1615412023-02-28T17:12:04Z Data considerations for predictive modeling applied to the discovery of bioactive natural products Xue, Hai Tao Stanley-Baker, Michael Kong, Adams Wai Kin Li, Hoi Leung Goh, Wilson Wen Bin School of Biological Sciences School of Humanities Lee Kong Chian School of Medicine (LKCMedicine) School of Computer Science and Engineering Center for Biomedical Informatics, NTU Science::Biological sciences Artificial Intelligence Data Integration Natural products (NPs) constitute a large reserve of bioactive compounds useful for drug development. Recent advances in high-throughput technologies facilitate functional analysis of therapeutic effects and NP-based drug discovery. However, the large amount of generated data is complex and difficult to analyze effectively. This limitation is increasingly surmounted by artificial intelligence (AI) techniques but more needs to be done. Here, we present and discuss two crucial issues limiting NP-AI drug discovery: the first is on knowledge and resource development (data integration) to bridge the gap between NPs and functional or therapeutic effects. The second issue is on NP-AI modeling considerations, limitations and challenges. National Research Foundation (NRF) Submitted/Accepted version This research project is supported by the National Research Foundation, Singapore, under its Industry Alignment Fund – Prepositioning (IAF-PP) Funding Initiative. 2022-09-07T02:09:33Z 2022-09-07T02:09:33Z 2022 Journal Article Xue, H. T., Stanley-Baker, M., Kong, A. W. K., Li, H. L. & Goh, W. W. B. (2022). Data considerations for predictive modeling applied to the discovery of bioactive natural products. Drug Discovery Today, 27(8), 2235-2243. https://dx.doi.org/10.1016/j.drudis.2022.05.009 1359-6446 https://hdl.handle.net/10356/161541 10.1016/j.drudis.2022.05.009 35577232 2-s2.0-85130491226 8 27 2235 2243 en Drug discovery today © 2022 Elsevier Ltd. All rights reserved. application/pdf |
| spellingShingle | Science::Biological sciences Artificial Intelligence Data Integration Xue, Hai Tao Stanley-Baker, Michael Kong, Adams Wai Kin Li, Hoi Leung Goh, Wilson Wen Bin Data considerations for predictive modeling applied to the discovery of bioactive natural products |
| title | Data considerations for predictive modeling applied to the discovery of bioactive natural products |
| title_full | Data considerations for predictive modeling applied to the discovery of bioactive natural products |
| title_fullStr | Data considerations for predictive modeling applied to the discovery of bioactive natural products |
| title_full_unstemmed | Data considerations for predictive modeling applied to the discovery of bioactive natural products |
| title_short | Data considerations for predictive modeling applied to the discovery of bioactive natural products |
| title_sort | data considerations for predictive modeling applied to the discovery of bioactive natural products |
| topic | Science::Biological sciences Artificial Intelligence Data Integration |
| url | https://hdl.handle.net/10356/161541 |
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