Academic Journal
Artificial intelligence advancements in monoclonal antibody development technology.
| Title: | Artificial intelligence advancements in monoclonal antibody development technology. |
|---|---|
| Authors: | Ammar, Manar, Samsonov, Mikhail, Gurylina, Evgeniya, Bayzigitov, Daniel |
| Source: | Frontiers in Immunology; 2026, p1-22, 22p |
| Subject Terms: | Artificial intelligence, Monoclonal antibodies, Immunotechnology, Protein models, Bioinformatics software, Antibody therapy |
| Abstract: | Monoclonal antibody–based therapeutics have become essential tools for treating infectious, autoimmune, and malignant diseases due to their high specificity and efficacy. As their clinical and scientific relevance continues to expand, the need for faster, more accurate and cost-effective development strategies has grown. Traditional laboratory-based methods for antibody design and improving remain reliable but are time-consuming, labor-intensive, and limited by experimental constraints. These challenges have driven a shift toward the integration of computational methods as a complementary approach for antibody engineering. The current review provides a simplified overall explanation of recent advancements in artificial intelligence (AI)-driven in silico tools used to accelerate and enhance the process of antibody discovery and optimization. We have systematically analyzed literature from clinical and research databases and summarized obtained data into a comprehensible overview. We highlighted how AI models contribute to sequence design, epitope-paratope predictions, affinity optimization, structural prediction and developability assessment. In conclusion, the most effective strategy for next-generation monoclonal antibody development relies on the integration of computational prediction and design tools followed by experimental validation. Combining AI-driven innovation with traditional laboratory methods represents a powerful and complementary approach for achieving accurate, efficient, and clinically relevant antibody therapeutics. [ABSTRACT FROM AUTHOR] |
| Copyright of Frontiers in Immunology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
| FullText | Text: Availability: 0 CustomLinks: – Url: https://resolver.ebsco.com/c/fiv2js/result?sid=EBSCO:edb&genre=article&issn=16643224&ISBN=&volume=&issue=&date=20260709&spage=1&pages=1-22&title=Frontiers in Immunology&atitle=Artificial%20intelligence%20advancements%20in%20monoclonal%20antibody%20development%20technology.&aulast=Ammar%2C%20Manar&id=DOI:10.3389/fimmu.2026.1802038 Name: Full Text Finder (for New FTF UI) (ns324271) Category: fullText Text: Full Text Finder MouseOverText: Full Text Finder |
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| Items | – Name: Title Label: Title Group: Ti Data: Artificial intelligence advancements in monoclonal antibody development technology. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Ammar%2C+Manar%22">Ammar, Manar</searchLink><br /><searchLink fieldCode="AR" term="%22Samsonov%2C+Mikhail%22">Samsonov, Mikhail</searchLink><br /><searchLink fieldCode="AR" term="%22Gurylina%2C+Evgeniya%22">Gurylina, Evgeniya</searchLink><br /><searchLink fieldCode="AR" term="%22Bayzigitov%2C+Daniel%22">Bayzigitov, Daniel</searchLink> – Name: TitleSource Label: Source Group: Src Data: Frontiers in Immunology; 2026, p1-22, 22p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Monoclonal+antibodies%22">Monoclonal antibodies</searchLink><br /><searchLink fieldCode="DE" term="%22Immunotechnology%22">Immunotechnology</searchLink><br /><searchLink fieldCode="DE" term="%22Protein+models%22">Protein models</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics+software%22">Bioinformatics software</searchLink><br /><searchLink fieldCode="DE" term="%22Antibody+therapy%22">Antibody therapy</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Monoclonal antibody–based therapeutics have become essential tools for treating infectious, autoimmune, and malignant diseases due to their high specificity and efficacy. As their clinical and scientific relevance continues to expand, the need for faster, more accurate and cost-effective development strategies has grown. Traditional laboratory-based methods for antibody design and improving remain reliable but are time-consuming, labor-intensive, and limited by experimental constraints. These challenges have driven a shift toward the integration of computational methods as a complementary approach for antibody engineering. The current review provides a simplified overall explanation of recent advancements in artificial intelligence (AI)-driven in silico tools used to accelerate and enhance the process of antibody discovery and optimization. We have systematically analyzed literature from clinical and research databases and summarized obtained data into a comprehensible overview. We highlighted how AI models contribute to sequence design, epitope-paratope predictions, affinity optimization, structural prediction and developability assessment. In conclusion, the most effective strategy for next-generation monoclonal antibody development relies on the integration of computational prediction and design tools followed by experimental validation. Combining AI-driven innovation with traditional laboratory methods represents a powerful and complementary approach for achieving accurate, efficient, and clinically relevant antibody therapeutics. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Frontiers in Immunology is the property of Frontiers Media S.A. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3389/fimmu.2026.1802038 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Monoclonal antibodies Type: general – SubjectFull: Immunotechnology Type: general – SubjectFull: Protein models Type: general – SubjectFull: Bioinformatics software Type: general – SubjectFull: Antibody therapy Type: general Titles: – TitleFull: Artificial intelligence advancements in monoclonal antibody development technology. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Ammar, Manar – PersonEntity: Name: NameFull: Samsonov, Mikhail – PersonEntity: Name: NameFull: Gurylina, Evgeniya – PersonEntity: Name: NameFull: Bayzigitov, Daniel IsPartOfRelationships: – BibEntity: Dates: – D: 09 M: 07 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16643224 Titles: – TitleFull: Frontiers in Immunology Type: main |
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