Academic Journal
Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review.
| Τίτλος: | Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review. |
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| Συγγραφείς: | Sampa, Masuda Begum, Aziz, Nor Hidayati Abdul |
| Πηγή: | Health Science Reports; Mar2026, Vol. 9 Issue 3, p1-9, 9p |
| Θεματικοί όροι: | Reinforcement learning, Drug design, Artificial intelligence, Probabilistic generative models, Optimization algorithms, Computer-assisted molecular design, Pharmacology, Drug discovery |
| Περίληψη: | Background and Aims: De novo drug design is the process of generating novel lead compounds that possess desirable pharmacological activities and optimal physicochemical properties for therapeutic development. In recent years, it has evolved into a key computational strategy for discovering and optimizing new therapeutic compounds. Reinforcement learning (RL), a branch of artificial intelligence, has emerged as a powerful tool to address the complex, sequential decision‐making processes involved in molecular generation. This study aims to review recent applications of RL in de novo drug design, highlight commonly used algorithms, identify major challenges, and discuss future research directions. Methods: A systematic literature review (SLR) was conducted following standard review procedures. Articles published between January 2017 and January 2024 were retrieved from Google Scholar using the keyword "Reinforcement Learning Techniques in de novo Drug Design." Studies were screened based on eligibility criteria, including relevance to RL‐based molecular generation, English language, and full‐text availability. Selected papers were analyzed to extract information on RL algorithms, design strategies, and application areas. Results: The reviewed studies demonstrate that RL has been successfully applied to molecular generation, optimization, and drug‐target design. Commonly used algorithms include policy‐gradient, actor–critic, and value‐based methods, often integrated with deep generative models such as recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), and graph neural networks (GNNs). RL frameworks have optimized properties like binding affinity, solubility, and bioavailability, while promoting molecular diversity. Despite these advances, challenges remain in sample efficiency, reward formulation, and interpretability. Conclusion: Reinforcement learning provides a robust framework for automated drug design, enabling intelligent exploration of chemical space and the generation of novel, bioactive compounds. However, further improvements in multi‐objective optimization, computational efficiency, and model transparency are essential for broader clinical applicability. Future research should focus on hybrid RL architectures and explainable AI techniques to bridge computational and experimental drug discovery. [ABSTRACT FROM AUTHOR] |
| Copyright of Health Science Reports is the property of Wiley-Blackwell 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 192598345 RelevancyScore: 1061 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1060.7568359375 |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sampa%2C+Masuda+Begum%22">Sampa, Masuda Begum</searchLink><br /><searchLink fieldCode="AR" term="%22Aziz%2C+Nor+Hidayati+Abdul%22">Aziz, Nor Hidayati Abdul</searchLink> – Name: TitleSource Label: Source Group: Src Data: Health Science Reports; Mar2026, Vol. 9 Issue 3, p1-9, 9p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Reinforcement+learning%22">Reinforcement learning</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+design%22">Drug design</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Probabilistic+generative+models%22">Probabilistic generative models</searchLink><br /><searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+molecular+design%22">Computer-assisted molecular design</searchLink><br /><searchLink fieldCode="DE" term="%22Pharmacology%22">Pharmacology</searchLink><br /><searchLink fieldCode="DE" term="%22Drug+discovery%22">Drug discovery</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background and Aims: De novo drug design is the process of generating novel lead compounds that possess desirable pharmacological activities and optimal physicochemical properties for therapeutic development. In recent years, it has evolved into a key computational strategy for discovering and optimizing new therapeutic compounds. Reinforcement learning (RL), a branch of artificial intelligence, has emerged as a powerful tool to address the complex, sequential decision‐making processes involved in molecular generation. This study aims to review recent applications of RL in de novo drug design, highlight commonly used algorithms, identify major challenges, and discuss future research directions. Methods: A systematic literature review (SLR) was conducted following standard review procedures. Articles published between January 2017 and January 2024 were retrieved from Google Scholar using the keyword "Reinforcement Learning Techniques in de novo Drug Design." Studies were screened based on eligibility criteria, including relevance to RL‐based molecular generation, English language, and full‐text availability. Selected papers were analyzed to extract information on RL algorithms, design strategies, and application areas. Results: The reviewed studies demonstrate that RL has been successfully applied to molecular generation, optimization, and drug‐target design. Commonly used algorithms include policy‐gradient, actor–critic, and value‐based methods, often integrated with deep generative models such as recurrent neural networks (RNNs), variational autoencoders (VAEs), generative adversarial networks (GANs), and graph neural networks (GNNs). RL frameworks have optimized properties like binding affinity, solubility, and bioavailability, while promoting molecular diversity. Despite these advances, challenges remain in sample efficiency, reward formulation, and interpretability. Conclusion: Reinforcement learning provides a robust framework for automated drug design, enabling intelligent exploration of chemical space and the generation of novel, bioactive compounds. However, further improvements in multi‐objective optimization, computational efficiency, and model transparency are essential for broader clinical applicability. Future research should focus on hybrid RL architectures and explainable AI techniques to bridge computational and experimental drug discovery. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Health Science Reports is the property of Wiley-Blackwell 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: 10.1002/hsr2.72132 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 1 Subjects: – SubjectFull: Reinforcement learning Type: general – SubjectFull: Drug design Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Probabilistic generative models Type: general – SubjectFull: Optimization algorithms Type: general – SubjectFull: Computer-assisted molecular design Type: general – SubjectFull: Pharmacology Type: general – SubjectFull: Drug discovery Type: general Titles: – TitleFull: Application of Reinforcement Learning Techniques in De Novo Drug Design: A Systematic Literature Review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Sampa, Masuda Begum – PersonEntity: Name: NameFull: Aziz, Nor Hidayati Abdul IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23988835 Numbering: – Type: volume Value: 9 – Type: issue Value: 3 Titles: – TitleFull: Health Science Reports Type: main |
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