Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach
Kirsten rat sarcoma viral oncogene homolog (KRAS) protein inhibitors are a promising class of therapeutics, but research on molecules that effectively penetrate the blood-brain barrier (BBB) remains limited, which is crucial for treating central nervous system (CNS) malignancies. Although molecular...
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Elsevier
2025-08-01
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| Series: | Journal of Pharmaceutical Analysis |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095177925001546 |
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| author | Xia Sheng Yike Gui Jie Yu Yitian Wang Zhenghao Li Xiaoya Zhang Yuxin Xing Yuqing Wang Zhaojun Li Mingyue Zheng Liquan Yang Xutong Li |
| author_facet | Xia Sheng Yike Gui Jie Yu Yitian Wang Zhenghao Li Xiaoya Zhang Yuxin Xing Yuqing Wang Zhaojun Li Mingyue Zheng Liquan Yang Xutong Li |
| author_sort | Xia Sheng |
| collection | DOAJ |
| description | Kirsten rat sarcoma viral oncogene homolog (KRAS) protein inhibitors are a promising class of therapeutics, but research on molecules that effectively penetrate the blood-brain barrier (BBB) remains limited, which is crucial for treating central nervous system (CNS) malignancies. Although molecular generation models have recently advanced drug discovery, they often overlook the complexity of biological and chemical factors, leaving room for improvement. In this study, we present a structure-constrained molecular generation workflow designed to optimize lead compounds for both drug efficacy and drug absorption properties. Our approach utilizes a variational autoencoder (VAE) generative model integrated with reinforcement learning for multi-objective optimization. This method specifically aims to enhance BBB permeability (BBBp) while maintaining high-affinity substructures of KRAS inhibitors. To support this, we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models. Additionally, we introduce two novel metrics, the knowledge-integrated reproduction score (KIRS) and the composite diversity score (CDS), to assess structural performance and biological relevance. Retrospective validation with KRAS inhibitors, AMG510 and MRTX849, demonstrates the framework’s effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications. This study provides a robust framework for accelerating the structural enhancement of lead compounds, advancing the drug development process across diverse targets. |
| format | Article |
| id | doaj-art-d5506a81bf6e44e09dfc4fb8402a98b2 |
| institution | Kabale University |
| issn | 2095-1779 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pharmaceutical Analysis |
| spelling | doaj-art-d5506a81bf6e44e09dfc4fb8402a98b22025-08-23T04:48:01ZengElsevierJournal of Pharmaceutical Analysis2095-17792025-08-0115810133710.1016/j.jpha.2025.101337Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approachXia Sheng0Yike Gui1Jie Yu2Yitian Wang3Zhenghao Li4Xiaoya Zhang5Yuxin Xing6Yuqing Wang7Zhaojun Li8Mingyue Zheng9Liquan Yang10Xutong Li11Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, ChinaDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; Nanjing University of Chinese Medicine, Nanjing, 210023, ChinaDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; Lingang Laboratory, Shanghai, 200031, China; School of Information Science and Technology, ShanghaiTech University, Shanghai, 201210, ChinaDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, ChinaDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; Shanghai Institute for Advanced Immunochemical Studies and School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, ChinaDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, ChinaDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, China; School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, Hangzhou, 330106, ChinaNanjing University of Chinese Medicine, Nanjing, 210023, China; Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, ChinaCollege of Computer and Information Engineering, Dezhou University, Dezhou, Shandong, 253023, ChinaDrug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Nanjing University of Chinese Medicine, Nanjing, 210023, China; School of Pharmaceutical Science and Technology, Hangzhou Institute for Advanced Study, Hangzhou, 330106, ChinaCollege of Agriculture and Biological Science, Dali University, Dali, Yunnan, 671003, China; Corresponding author.Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Corresponding author. Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.Kirsten rat sarcoma viral oncogene homolog (KRAS) protein inhibitors are a promising class of therapeutics, but research on molecules that effectively penetrate the blood-brain barrier (BBB) remains limited, which is crucial for treating central nervous system (CNS) malignancies. Although molecular generation models have recently advanced drug discovery, they often overlook the complexity of biological and chemical factors, leaving room for improvement. In this study, we present a structure-constrained molecular generation workflow designed to optimize lead compounds for both drug efficacy and drug absorption properties. Our approach utilizes a variational autoencoder (VAE) generative model integrated with reinforcement learning for multi-objective optimization. This method specifically aims to enhance BBB permeability (BBBp) while maintaining high-affinity substructures of KRAS inhibitors. To support this, we incorporate a specialized KRAS BBB predictor based on active learning and an affinity predictor employing comparative learning models. Additionally, we introduce two novel metrics, the knowledge-integrated reproduction score (KIRS) and the composite diversity score (CDS), to assess structural performance and biological relevance. Retrospective validation with KRAS inhibitors, AMG510 and MRTX849, demonstrates the framework’s effectiveness in optimizing BBBp and highlights its potential for real-world drug development applications. This study provides a robust framework for accelerating the structural enhancement of lead compounds, advancing the drug development process across diverse targets.http://www.sciencedirect.com/science/article/pii/S2095177925001546KRAS inhibitorsDrug designBlood-brain barrier permeabilityMolecular optimizationDeep learningGeneration models |
| spellingShingle | Xia Sheng Yike Gui Jie Yu Yitian Wang Zhenghao Li Xiaoya Zhang Yuxin Xing Yuqing Wang Zhaojun Li Mingyue Zheng Liquan Yang Xutong Li Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach Journal of Pharmaceutical Analysis KRAS inhibitors Drug design Blood-brain barrier permeability Molecular optimization Deep learning Generation models |
| title | Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach |
| title_full | Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach |
| title_fullStr | Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach |
| title_full_unstemmed | Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach |
| title_short | Optimizing blood-brain barrier permeability in KRAS inhibitors: A structure-constrained molecular generation approach |
| title_sort | optimizing blood brain barrier permeability in kras inhibitors a structure constrained molecular generation approach |
| topic | KRAS inhibitors Drug design Blood-brain barrier permeability Molecular optimization Deep learning Generation models |
| url | http://www.sciencedirect.com/science/article/pii/S2095177925001546 |
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