AI in drug discovery and its clinical relevance
<p dir="ltr">The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the...
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2023
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| _version_ | 1864513564161605632 |
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| author | Rizwan Qureshi (15279193) |
| author2 | Muhammad Irfan (255476) Taimoor Muzaffar Gondal (16500266) Sheheryar Khan (16500267) Jia Wu (169990) Muhammad Usman Hadi (16500270) John Heymach (5343626) Xiuning Le (11850350) Hong Yan (27984) Tanvir Alam (638619) |
| author2_role | author author author author author author author author author |
| author_facet | Rizwan Qureshi (15279193) Muhammad Irfan (255476) Taimoor Muzaffar Gondal (16500266) Sheheryar Khan (16500267) Jia Wu (169990) Muhammad Usman Hadi (16500270) John Heymach (5343626) Xiuning Le (11850350) Hong Yan (27984) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rizwan Qureshi (15279193) Muhammad Irfan (255476) Taimoor Muzaffar Gondal (16500266) Sheheryar Khan (16500267) Jia Wu (169990) Muhammad Usman Hadi (16500270) John Heymach (5343626) Xiuning Le (11850350) Hong Yan (27984) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2023-06-25T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.heliyon.2023.e17575 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/AI_in_drug_discovery_and_its_clinical_relevance/23633784 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Pharmacology and pharmaceutical sciences Information and computing sciences Artificial intelligence Artificial intelligence Biotechnology Graph neural networks Molecule representation Reinforcement learning Drug discovery Molecular dynamics simulation |
| dc.title.none.fl_str_mv | AI in drug discovery and its clinical relevance |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of <i>de novo</i> design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.</p><h2>Other Information</h2><p dir="ltr">Published in: Heliyon<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1016/j.heliyon.2023.e17575" target="_blank">https://doi.org/10.1016/j.heliyon.2023.e17575</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_0bb2346a0f9ed135f430f42e08f7814a |
| identifier_str_mv | 10.1016/j.heliyon.2023.e17575 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23633784 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | AI in drug discovery and its clinical relevanceRizwan Qureshi (15279193)Muhammad Irfan (255476)Taimoor Muzaffar Gondal (16500266)Sheheryar Khan (16500267)Jia Wu (169990)Muhammad Usman Hadi (16500270)John Heymach (5343626)Xiuning Le (11850350)Hong Yan (27984)Tanvir Alam (638619)Biomedical and clinical sciencesPharmacology and pharmaceutical sciencesInformation and computing sciencesArtificial intelligenceArtificial intelligenceBiotechnologyGraph neural networksMolecule representationReinforcement learningDrug discoveryMolecular dynamics simulation<p dir="ltr">The COVID-19 pandemic has emphasized the need for novel drug discovery process. However, the journey from conceptualizing a drug to its eventual implementation in clinical settings is a long, complex, and expensive process, with many potential points of failure. Over the past decade, a vast growth in medical information has coincided with advances in computational hardware (cloud computing, GPUs, and TPUs) and the rise of deep learning. Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of <i>de novo</i> design and prediction of a drug's likely properties. Open-source databases and AI-based software tools that facilitate drug design are discussed along with their associated problems of molecule representation, data collection, complexity, labeling, and disparities among labels. How contemporary AI methods, such as graph neural networks, reinforcement learning, and generated models, along with structure-based methods, (i.e., molecular dynamics simulations and molecular docking) can contribute to drug discovery applications and analysis of drug responses is also explored. Finally, recent developments and investments in AI-based start-up companies for biotechnology, drug design and their current progress, hopes and promotions are discussed in this article.</p><h2>Other Information</h2><p dir="ltr">Published in: Heliyon<br>License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://doi.org/10.1016/j.heliyon.2023.e17575" target="_blank">https://doi.org/10.1016/j.heliyon.2023.e17575</a></p>2023-06-25T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.heliyon.2023.e17575https://figshare.com/articles/journal_contribution/AI_in_drug_discovery_and_its_clinical_relevance/23633784CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/236337842023-06-25T00:00:00Z |
| spellingShingle | AI in drug discovery and its clinical relevance Rizwan Qureshi (15279193) Biomedical and clinical sciences Pharmacology and pharmaceutical sciences Information and computing sciences Artificial intelligence Artificial intelligence Biotechnology Graph neural networks Molecule representation Reinforcement learning Drug discovery Molecular dynamics simulation |
| status_str | publishedVersion |
| title | AI in drug discovery and its clinical relevance |
| title_full | AI in drug discovery and its clinical relevance |
| title_fullStr | AI in drug discovery and its clinical relevance |
| title_full_unstemmed | AI in drug discovery and its clinical relevance |
| title_short | AI in drug discovery and its clinical relevance |
| title_sort | AI in drug discovery and its clinical relevance |
| topic | Biomedical and clinical sciences Pharmacology and pharmaceutical sciences Information and computing sciences Artificial intelligence Artificial intelligence Biotechnology Graph neural networks Molecule representation Reinforcement learning Drug discovery Molecular dynamics simulation |