De novo molecular design is the process of searching chemical space for drug-like molecules with desired properties, and deep learning has been recognized as a promising solution. In this study, I developed an effective computational method called Scoring-Assisted Generative Exploration (SAGE) to enhance chemical diversity and property optimization through virtual synthesis simulation, the generation of bridged bicyclic rings, and multiple scoring models for drug-likeness. In six protein targets, SAGE generated molecules with high scores within reasonable numbers of steps by optimizing target specificity without a constraint and even with multiple constraints such as synthetic accessibility, solubility, and metabolic stability. Furthermore, I suggested a top-ranked molecule with SAGE as dual inhibitors of acetylcholinesterase and monoamine oxidase B through multiple desired property optimization. Therefore, SAGE can generate molecules with desired properties by optimizing multiple properties simultaneously, indicating the importance of de novo design strategies in the future of drug discovery and development. The scientific contribution of this study lies in the development of the Scoring-Assisted Generative Exploration (SAGE) method, a novel computational approach that significantly enhances de novo molecular design. SAGE uniquely integrates virtual synthesis simulation, the generation of complex bridged bicyclic rings, and multiple scoring models to optimize drug-like properties comprehensively. By efficiently generating molecules that meet a broad spectrum of pharmacological criteria—including target specificity, synthetic accessibility, solubility, and metabolic stability—within a reasonable number of steps, SAGE represents a substantial advancement over traditional methods. Additionally, the application of SAGE to discover dual inhibitors for acetylcholinesterase and monoamine oxidase B not only demonstrates its potential to streamline and enhance the drug development process but also highlights its capacity to create more effective and precisely targeted therapies. This study emphasizes the critical and evolving role of de novo design strategies in reshaping the future of drug discovery and development, providing promising avenues for innovative therapeutic discoveries.

中文翻译：

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评分辅助生成探索（SAGE）的发展及其在乙酰胆碱酯酶和单胺氧化酶 B 双抑制剂设计中的应用


从头分子设计是在化学空间中搜索具有所需特性的类药分子的过程，深度学习已被认为是一种有前途的解决方案。在这项研究中，我开发了一种称为评分辅助生成探索（SAGE）的有效计算方法，通过虚拟合成模拟、桥联双环的生成和药物相似性的多个评分模型来增强化学多样性和性质优化。在六个蛋白质靶标中，SAGE 通过在无限制甚至具有合成可及性、溶解度和代谢稳定性等多重限制的情况下优化靶标特异性，在合理的步骤数内生成了得分较高的分子。此外，我通过多种所需的性能优化，提出了将 SAGE 作为乙酰胆碱酯酶和单胺氧化酶 B 双重抑制剂的顶级分子。因此，SAGE 可以通过同时优化多种特性来生成具有所需特性的分子，这表明从头设计策略在未来药物发现和开发中的重要性。这项研究的科学贡献在于开发了评分辅助生成探索（SAGE）方法，这是一种显着增强从头分子设计的新型计算方法。 SAGE独特地集成了虚拟合成模拟、复杂桥联双环的生成和多种评分模型，以全面优化药物的性质。 通过在合理的步骤内有效生成满足广泛药理学标准（包括靶标特异性、合成可及性、溶解度和代谢稳定性）的分子，SAGE 代表了传统方法的重大进步。此外，应用 SAGE 发现乙酰胆碱酯酶和单胺氧化酶 B 的双重抑制剂不仅证明了其简化和增强药物开发过程的潜力，而且还凸显了其创造更有效和更精确的靶向疗法的能力。这项研究强调了从头设计策略在重塑药物发现和开发的未来方面的关键和不断发展的作用，为创新治疗发现提供了有希望的途径。