Machine learning for synergistic network pharmacology : a comprehensive overview
© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissionsoup.com..
Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2023 |
|---|---|
| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
|---|---|
| Enthalten in: |
Briefings in bioinformatics - 24(2023), 3 vom: 19. Mai |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Noor, Fatima [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
Artificial intelligence |
|---|
| Anmerkungen: |
Date Completed 22.05.2023 Date Revised 23.05.2023 published: Print Citation Status MEDLINE |
|---|
| doi: |
10.1093/bib/bbad120 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM355365936 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM355365936 | ||
| 003 | DE-627 | ||
| 005 | 20231226064208.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231226s2023 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1093/bib/bbad120 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1184.xml |
| 035 | |a (DE-627)NLM355365936 | ||
| 035 | |a (NLM)37031957 | ||
| 035 | |a (PII)bbad120 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Noor, Fatima |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Machine learning for synergistic network pharmacology |b a comprehensive overview |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Completed 22.05.2023 | ||
| 500 | |a Date Revised 23.05.2023 | ||
| 500 | |a published: Print | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © The Author(s) 2023. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a Network pharmacology is an emerging area of systematic drug research that attempts to understand drug actions and interactions with multiple targets. Network pharmacology has changed the paradigm from 'one-target one-drug' to highly potent 'multi-target drug'. Despite that, this synergistic approach is currently facing many challenges particularly mining effective information such as drug targets, mechanism of action, and drug and organism interaction from massive, heterogeneous data. To overcome bottlenecks in multi-target drug discovery, computational algorithms are highly welcomed by scientific community. Machine learning (ML) and especially its subfield deep learning (DL) have seen impressive advances. Techniques developed within these fields are now able to analyze and learn from huge amounts of data in disparate formats. In terms of network pharmacology, ML can improve discovery and decision making from big data. Opportunities to apply ML occur in all stages of network pharmacology research. Examples include screening of biologically active small molecules, target identification, metabolic pathways identification, protein-protein interaction network analysis, hub gene analysis and finding binding affinity between compounds and target proteins. This review summarizes the premier algorithmic concepts of ML in network pharmacology and forecasts future opportunities, potential applications as well as several remaining challenges of implementing ML in network pharmacology. To our knowledge, this study provides the first comprehensive assessment of ML approaches in network pharmacology, and we hope that it encourages additional efforts toward the development and acceptance of network pharmacology in the pharmaceutical industry | ||
| 650 | 4 | |a Review | |
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a artificial intelligence | |
| 650 | 4 | |a deep learning | |
| 650 | 4 | |a drug discovery | |
| 650 | 4 | |a machine learning | |
| 650 | 4 | |a network pharmacology | |
| 650 | 7 | |a Proteins |2 NLM | |
| 700 | 1 | |a Asif, Muhammad |e verfasserin |4 aut | |
| 700 | 1 | |a Ashfaq, Usman Ali |e verfasserin |4 aut | |
| 700 | 1 | |a Qasim, Muhammad |e verfasserin |4 aut | |
| 700 | 1 | |a Tahir Ul Qamar, Muhammad |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Briefings in bioinformatics |d 2000 |g 24(2023), 3 vom: 19. Mai |w (DE-627)NLM11366883X |x 1477-4054 |7 nnns |
| 773 | 1 | 8 | |g volume:24 |g year:2023 |g number:3 |g day:19 |g month:05 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1093/bib/bbad120 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 24 |j 2023 |e 3 |b 19 |c 05 | ||