Data considerations for predictive modeling applied to the discovery of bioactive natural products

Hai Tao Xue; Michael Stanley-Baker; Adams Wai Kin Kong; Hoi Leung Li; Wilson Wen Bin Goh

doi:10.1016/j.drudis.2022.05.009

Data considerations for predictive modeling applied to the discovery of bioactive natural products

Hai Tao Xue, Michael Stanley-Baker, Adams Wai Kin Kong, Hoi Leung Li, Wilson Wen Bin Goh^*

^*Corresponding author for this work

Nanyang Technological University

Research output: Contribution to journal › Review article › peer-review

11 Citations (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	2235-2243
Number of pages	9
Journal	Drug Discovery Today
Volume	27
Issue number	8
DOIs	https://doi.org/10.1016/j.drudis.2022.05.009
Publication status	Published - Aug 2022
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2022 Elsevier Ltd

ASJC Scopus Subject Areas

Pharmacology
Drug Discovery

Keywords

Artificial intelligence
Data integration
Data mining
Database
Deep learning
Knowledge discovery
Natural products

Access to Document

10.1016/j.drudis.2022.05.009

Cite this

@article{3382ddc788d64ea6a6d61fdae0547d65,

title = "Data considerations for predictive modeling applied to the discovery of bioactive natural products",

abstract = "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.",

keywords = "Artificial intelligence, Data integration, Data mining, Database, Deep learning, Knowledge discovery, Natural products",

author = "Xue, {Hai Tao} and Michael Stanley-Baker and Kong, {Adams Wai Kin} and Li, {Hoi Leung} and Goh, {Wilson Wen Bin}",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier Ltd",

year = "2022",

month = aug,

doi = "10.1016/j.drudis.2022.05.009",

language = "English",

volume = "27",

pages = "2235--2243",

journal = "Drug Discovery Today",

issn = "1359-6446",

publisher = "Elsevier Limited",

number = "8",

}

TY - JOUR

T1 - Data considerations for predictive modeling applied to the discovery of bioactive natural products

AU - Xue, Hai Tao

AU - Stanley-Baker, Michael

AU - Kong, Adams Wai Kin

AU - Li, Hoi Leung

AU - Goh, Wilson Wen Bin

PY - 2022/8

Y1 - 2022/8

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Data integration

KW - Data mining

KW - Database

KW - Deep learning

KW - Knowledge discovery

KW - Natural products

UR - http://www.scopus.com/inward/record.url?scp=85130491226&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85130491226&partnerID=8YFLogxK

U2 - 10.1016/j.drudis.2022.05.009

DO - 10.1016/j.drudis.2022.05.009

M3 - Review article

C2 - 35577232

AN - SCOPUS:85130491226

SN - 1359-6446

VL - 27

SP - 2235

EP - 2243

JO - Drug Discovery Today

JF - Drug Discovery Today

IS - 8

ER -

Data considerations for predictive modeling applied to the discovery of bioactive natural products

Abstract

Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

Other files and links

Fingerprint

Cite this