Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery
Abstract Spleen tyrosine kinase (Syk) is a crucial mediator of inflammatory processes and a promising therapeutic target for the management of autoimmune disorders, such as immune thrombocytopenia. While several Syk inhibitors are known to date, their efficacy and safety profiles remain suboptimal,...
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| Format: | Article |
| Language: | English |
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BMC
2025-04-01
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| Series: | Journal of Cheminformatics |
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| Online Access: | https://doi.org/10.1186/s13321-025-00998-2 |
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| author | Maria Zavadskaya Anastasia Orlova Andrei Dmitrenko Vladimir Vinogradov |
| author_facet | Maria Zavadskaya Anastasia Orlova Andrei Dmitrenko Vladimir Vinogradov |
| author_sort | Maria Zavadskaya |
| collection | DOAJ |
| description | Abstract Spleen tyrosine kinase (Syk) is a crucial mediator of inflammatory processes and a promising therapeutic target for the management of autoimmune disorders, such as immune thrombocytopenia. While several Syk inhibitors are known to date, their efficacy and safety profiles remain suboptimal, necessitating the exploration of novel compounds. The study introduces a novel deep reinforcement learning strategy for drug discovery, specifically designed to identify new Syk inhibitors. The approach integrates quantitative structure–activity relationship (QSAR) predictions with generative modelling, employing a stacking-ensemble model that achieves a correlation coefficient of 0.78. From over 78,000 molecules generated by this methodology, we identified 139 promising candidates with high predicted potency, binding affinity and optimal drug-likeness properties, demonstrating structural novelty while maintaining essential Syk inhibitor characteristics. Our approach establishes a versatile framework for accelerated drug discovery, which is particularly valuable for the development of rare disease therapeutics. Scientific contribution The study presents the first application of QSAR-guided reinforcement learning for Syk inhibitor discovery, yielding structurally novel candidates with predicted high potency. The presented methodology can be adapted for other therapeutic targets, potentially accelerating the drug development process. |
| format | Article |
| id | doaj-art-3f80a30a440b4fe2a9ec7c0049650a80 |
| institution | OA Journals |
| issn | 1758-2946 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj-art-3f80a30a440b4fe2a9ec7c0049650a802025-08-20T02:17:49ZengBMCJournal of Cheminformatics1758-29462025-04-0117111110.1186/s13321-025-00998-2Integrating QSAR modelling with reinforcement learning for Syk inhibitor discoveryMaria Zavadskaya0Anastasia Orlova1Andrei Dmitrenko2Vladimir Vinogradov3 Center for AI in Chemistry, ITMO University Center for AI in Chemistry, ITMO University Center for AI in Chemistry, ITMO University Center for AI in Chemistry, ITMO UniversityAbstract Spleen tyrosine kinase (Syk) is a crucial mediator of inflammatory processes and a promising therapeutic target for the management of autoimmune disorders, such as immune thrombocytopenia. While several Syk inhibitors are known to date, their efficacy and safety profiles remain suboptimal, necessitating the exploration of novel compounds. The study introduces a novel deep reinforcement learning strategy for drug discovery, specifically designed to identify new Syk inhibitors. The approach integrates quantitative structure–activity relationship (QSAR) predictions with generative modelling, employing a stacking-ensemble model that achieves a correlation coefficient of 0.78. From over 78,000 molecules generated by this methodology, we identified 139 promising candidates with high predicted potency, binding affinity and optimal drug-likeness properties, demonstrating structural novelty while maintaining essential Syk inhibitor characteristics. Our approach establishes a versatile framework for accelerated drug discovery, which is particularly valuable for the development of rare disease therapeutics. Scientific contribution The study presents the first application of QSAR-guided reinforcement learning for Syk inhibitor discovery, yielding structurally novel candidates with predicted high potency. The presented methodology can be adapted for other therapeutic targets, potentially accelerating the drug development process.https://doi.org/10.1186/s13321-025-00998-2Generative designDrug discoveryQSARMachine learningSyk inhibitorsReinforcement learning |
| spellingShingle | Maria Zavadskaya Anastasia Orlova Andrei Dmitrenko Vladimir Vinogradov Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery Journal of Cheminformatics Generative design Drug discovery QSAR Machine learning Syk inhibitors Reinforcement learning |
| title | Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery |
| title_full | Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery |
| title_fullStr | Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery |
| title_full_unstemmed | Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery |
| title_short | Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery |
| title_sort | integrating qsar modelling with reinforcement learning for syk inhibitor discovery |
| topic | Generative design Drug discovery QSAR Machine learning Syk inhibitors Reinforcement learning |
| url | https://doi.org/10.1186/s13321-025-00998-2 |
| work_keys_str_mv | AT mariazavadskaya integratingqsarmodellingwithreinforcementlearningforsykinhibitordiscovery AT anastasiaorlova integratingqsarmodellingwithreinforcementlearningforsykinhibitordiscovery AT andreidmitrenko integratingqsarmodellingwithreinforcementlearningforsykinhibitordiscovery AT vladimirvinogradov integratingqsarmodellingwithreinforcementlearningforsykinhibitordiscovery |