Generative artificial intelligence based models optimization towards molecule design enhancement
Abstract Generative artificial intelligence (GenAI) models have emerged as a transformative tool for addressing the complex challenges of drug discovery, enabling the design of structurally diverse, chemically valid, and functionally relevant molecules. Despite significant advancements, the rapid ex...
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| Format: | Article |
| Language: | English |
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BMC
2025-08-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-01059-4 |
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| _version_ | 1849331973826281472 |
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| author | Tarek Khater Sara Awni Alkhatib Aamna AlShehhi Charalampos Pitsalidis Anna Maria Pappa Son Tung Ngo Vincent Chan Vi Khanh Truong |
| author_facet | Tarek Khater Sara Awni Alkhatib Aamna AlShehhi Charalampos Pitsalidis Anna Maria Pappa Son Tung Ngo Vincent Chan Vi Khanh Truong |
| author_sort | Tarek Khater |
| collection | DOAJ |
| description | Abstract Generative artificial intelligence (GenAI) models have emerged as a transformative tool for addressing the complex challenges of drug discovery, enabling the design of structurally diverse, chemically valid, and functionally relevant molecules. Despite significant advancements, the rapid expansion of GenAI applications still faces challenges related to prediction accuracy, molecular validity, and optimization for drug-like properties. This review provides a comprehensive analysis of recent techniques and strategies aimed at enhancing the performance of GenAI models in molecular design. We explore key generative architectures, including variational autoencoders, generative adversarial networks, and transformer-based models, highlighting their unique contributions to drug discovery. Additionally, we discuss critical advancements such as reinforcement learning, multi-objective optimization, and the integration of domain-specific chemical knowledge, which collectively enhance molecular validity, novelty, and drug-likeness. Also, the review examines persistent challenges, including data quality limitations, model interpretability, and the need for improved objective functions, while offering insights into future research directions. By mapping the evolving landscape of GenAI-driven molecular design and providing strategic guidance for overcoming existing limitations, this review serves as an essential resource for researchers leveraging GenAI in drug discovery. |
| format | Article |
| id | doaj-art-5e2cbf976ca84c8ab1288164cc3566f4 |
| institution | Kabale University |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-5e2cbf976ca84c8ab1288164cc3566f42025-08-20T03:46:21ZengBMCJournal of Cheminformatics1758-29462025-08-0117112010.1186/s13321-025-01059-4Generative artificial intelligence based models optimization towards molecule design enhancementTarek Khater0Sara Awni Alkhatib1Aamna AlShehhi2Charalampos Pitsalidis3Anna Maria Pappa4Son Tung Ngo5Vincent Chan6Vi Khanh Truong7Department of Biomedical Engineering and Biotechnology, Khalifa UniversityDepartment of Biomedical Engineering and Biotechnology, Khalifa UniversityDepartment of Biomedical Engineering and Biotechnology, Khalifa UniversityDepartment of Physics, Khalifa UniversityDepartment of Biomedical Engineering and Biotechnology, Khalifa UniversityLaboratory of Biophysics, Institute for Advanced Study in Technology, Ton Duc Thang UniversityDepartment of Biomedical Engineering and Biotechnology, Khalifa UniversityDepartment of Biomedical Engineering and Biotechnology, Khalifa UniversityAbstract Generative artificial intelligence (GenAI) models have emerged as a transformative tool for addressing the complex challenges of drug discovery, enabling the design of structurally diverse, chemically valid, and functionally relevant molecules. Despite significant advancements, the rapid expansion of GenAI applications still faces challenges related to prediction accuracy, molecular validity, and optimization for drug-like properties. This review provides a comprehensive analysis of recent techniques and strategies aimed at enhancing the performance of GenAI models in molecular design. We explore key generative architectures, including variational autoencoders, generative adversarial networks, and transformer-based models, highlighting their unique contributions to drug discovery. Additionally, we discuss critical advancements such as reinforcement learning, multi-objective optimization, and the integration of domain-specific chemical knowledge, which collectively enhance molecular validity, novelty, and drug-likeness. Also, the review examines persistent challenges, including data quality limitations, model interpretability, and the need for improved objective functions, while offering insights into future research directions. By mapping the evolving landscape of GenAI-driven molecular design and providing strategic guidance for overcoming existing limitations, this review serves as an essential resource for researchers leveraging GenAI in drug discovery.https://doi.org/10.1186/s13321-025-01059-4Generative AIMolecular designOptimizationReinforcement learningChemical informaticsDrug discovery |
| spellingShingle | Tarek Khater Sara Awni Alkhatib Aamna AlShehhi Charalampos Pitsalidis Anna Maria Pappa Son Tung Ngo Vincent Chan Vi Khanh Truong Generative artificial intelligence based models optimization towards molecule design enhancement Journal of Cheminformatics Generative AI Molecular design Optimization Reinforcement learning Chemical informatics Drug discovery |
| title | Generative artificial intelligence based models optimization towards molecule design enhancement |
| title_full | Generative artificial intelligence based models optimization towards molecule design enhancement |
| title_fullStr | Generative artificial intelligence based models optimization towards molecule design enhancement |
| title_full_unstemmed | Generative artificial intelligence based models optimization towards molecule design enhancement |
| title_short | Generative artificial intelligence based models optimization towards molecule design enhancement |
| title_sort | generative artificial intelligence based models optimization towards molecule design enhancement |
| topic | Generative AI Molecular design Optimization Reinforcement learning Chemical informatics Drug discovery |
| url | https://doi.org/10.1186/s13321-025-01059-4 |
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