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<p>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&#xa0;approach establishes a versatile framework for accelerated drug discovery, which is particularly valuable for the development of rare disease therapeutics.</p><p><b>Scientific contribution</b></p><p>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.</p>

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Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月15日

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  • Maria Zavadskaya,
  • Anastasia Orlova,
  • Andrei Dmitrenko,
  • Vladimir Vinogradov

摘要

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.